Voice Command Integration into Augmented Reality Systems and Virtual Reality Systems

ABSTRACT

In one embodiment, a method includes receiving, by a XR display device, a gesture-based input from a first user of the XR display device, processing, using a gesture-detection model, the gesture-based input to identify a first gesture, receiving, by the XR display device, an audio input from the first user, where the audio input includes a first voice command, processing, using a natural-language model, the audio input to identify one or more intents or one or more slots associated with the first voice command, determining whether the identified first gesture matches the first voice command, and executing, responsive to the identified first gesture matching the first voice command and by the XR display device, a first task corresponding to the first voice command based on the identified first gesture and the identified one or more intents or one or more slots.

PRIORITY

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application No. 63/272621, filed 27 Oct. 2021, whichis incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for augmented reality systems and virtual reality systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

Standard virtual reality systems use either virtual reality headsets ormulti-projected environments to generate realistic images, sounds andother sensations that simulate a user's physical presence in a virtualenvironment. A person using virtual reality equipment is able to lookaround the artificial world, move around in it, and interact withvirtual features or items. The effect is commonly created by VR headsetsconsisting of a head-mounted display with a small screen in front of theeyes but can also be created through specially designed rooms withmultiple large screens. Virtual reality typically incorporates auditoryand video feedback but may also allow other types of sensory and forcefeedback through haptic technology.

Augmented reality is an interactive experience that combines the realworld and computer-generated content. The content can span multiplesensory modalities, including visual, auditory, haptic, somatosensoryand olfactory. Augmented reality can be defined as a system thatincorporates three basic features: a combination of real and virtualworlds, real-time interaction, and accurate 3D registration of virtualand real objects. The overlaid sensory information can be constructive(i.e. additive to the natural environment), or destructive (i.e. maskingof the natural environment). This experience is seamlessly interwovenwith the physical world such that it is perceived as an immersive aspectof the real environment.

Virtual reality (VR) and augmented reality (AR) applications areapplications that make use of an immersive sensory experience thatdigitally simulates a virtual environment or virtual objects in areal-world environment. Applications have been developed in a variety ofdomains, such as education, architectural and urban design, digitalmarketing and activism, engineering and robotics, entertainment, virtualcommunities, fine arts, healthcare and clinical therapies, heritage andarchaeology, occupational safety, social science and psychology.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with the assistant system via user inputs of variousmodalities (e.g., audio, voice, text, image, video, gesture, motion,location, orientation) in stateful and multi-turn conversations toreceive assistance from the assistant system. As an example and not byway of limitation, the assistant system may support mono-modal inputs(e.g., only voice inputs), multi-modal inputs (e.g., voice inputs andtext inputs), hybrid/multi-modal inputs, or any combination thereof.User inputs provided by a user may be associated with particularassistant-related tasks, and may include, for example, user requests(e.g., verbal requests for information or performance of an action),user interactions with an assistant application associated with theassistant system (e.g., selection of UI elements via touch or gesture),or any other type of suitable user input that may be detected andunderstood by the assistant system (e.g., user movements detected by theclient device of the user). The assistant system may create and store auser profile comprising both personal and contextual informationassociated with the user. In particular embodiments, the assistantsystem may analyze the user input using natural-language understanding(NLU). The analysis may be based on the user profile of the user formore personalized and context-aware understanding. The assistant systemmay resolve entities associated with the user input based on theanalysis. In particular embodiments, the assistant system may interactwith different agents to obtain information or services that areassociated with the resolved entities. The assistant system may generatea response for the user regarding the information or services by usingnatural-language generation (NLG). Through the interaction with theuser, the assistant system may use dialog-management techniques tomanage and advance the conversation flow with the user. In particularembodiments, the assistant system may further assist the user toeffectively and efficiently digest the obtained information bysummarizing the information. The assistant system may also assist theuser to be more engaging with an online social network by providingtools that help the user interact with the online social network (e.g.,creating posts, comments, messages). The assistant system mayadditionally assist the user to manage different tasks such as keepingtrack of events. In particular embodiments, the assistant system mayproactively execute, without a user input, tasks that are relevant touser interests and preferences based on the user profile, at a timerelevant for the user. In particular embodiments, the assistant systemmay check privacy settings to ensure that accessing a user's profile orother user information and executing different tasks are permittedsubject to the user's privacy settings.

In particular embodiments, the assistant system may assist the user viaa hybrid architecture built upon both client-side processes andserver-side processes. The client-side processes and the server-sideprocesses may be two parallel workflows for processing a user input andproviding assistance to the user. In particular embodiments, theclient-side processes may be performed locally on a client systemassociated with a user. By contrast, the server-side processes may beperformed remotely on one or more computing systems. In particularembodiments, an arbitrator on the client system may coordinate receivinguser input (e.g., an audio signal), determine whether to use aclient-side process, a server-side process, or both, to respond to theuser input, and analyze the processing results from each process. Thearbitrator may instruct agents on the client-side or server-side toexecute tasks associated with the user input based on the aforementionedanalyses. The execution results may be further rendered as output to theclient system. By leveraging both client-side and server-side processes,the assistant system can effectively assist a user with optimal usage ofcomputing resources while at the same time protecting user privacy andenhancing security.

In particular embodiments, a client system may implement voice commandswithin an augmented reality (AR), virtual reality (VR), mixed reality(MR), or extended reality (XR) environment via a voice SDK, which allowsXR applications to easily integrate voice commands on XR devices (e.g.,the client system). In particular embodiments, XR may be one or more ofa combination of AR, VR, or MR. As an example and not by way oflimitation, XR may include elements of AR or VR or MR. In particularembodiments, the client system may implement voice commands incombination with gestures within XR environments via a voice SDK. As XRcontent becomes more immersive, voice integration may make the contentmore engaging to interact with various applications. That is, peopletypically interact with the real world using their voice and hands.Currently, navigating through XR content may be cumbersome as the usermay need to navigate through unfamiliar nested menus. As an example andnot by way of limitation, a user may need to click through variousvirtual menus by ray-casting from a controller or inputting text througha virtual keyboard to navigate to a desired destination. To improve uponthe user experience, the XR system may implement voice commands,combined with one or more other modalities (e.g., gesture, pose, eyegaze, etc.) to allow a user to perform voice navigation/search, voiceFAQ, and voice-driven gameplay & experiences.

In particular embodiments, a client system embodied as an augmentedreality headset, virtual reality headset, or extended reality headsetmay perform the method of processing a voice command. In particularembodiments, the client system may receive a gesture-based input from afirst user of the client system. In particular embodiments, the clientsystem may process, using a gesture-detection model, the gesture-basedinput to identify a first gesture. In particular embodiments, the clientsystem may receive an audio input from the first user. In particularembodiments, the audio input comprises a first voice command. Inparticular embodiments, client system may process, using anatural-language model, the audio input to identify one or more intentsor one or more slots associated with the first voice command. Inparticular embodiments, client system may determine whether theidentified first gesture matches the first voice command. In particularembodiments, client system may execute, responsive to the identifiedfirst gesture matching the first voice command and by the XR displaydevice, a first task corresponding to the first voice command based onthe identified first gesture and the identified one or more intents orone or more slots. Particular embodiments may repeat one or more stepsof the method, where appropriate. Although this disclosure describes andillustrates particular steps of the method as occurring in a particularorder, this disclosure contemplates any suitable steps of the method ofoccurring in any suitable order. Moreover, although this disclosuredescribes and illustrates an example method for processing a voicecommand including the particular steps of the method, this disclosurecontemplates any suitable method for processing a voice commandincluding any suitable steps, which may include all, some, or none ofthe steps of the method, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method, this disclosurecontemplates any suitable combination of any suitable components,devices, or systems carrying out any suitable steps of the method.

Certain technical challenges exist for navigating through XR content.One technical challenge may include navigating through the unfamiliarnested menus. Another technical challenge may include a user needing toclick through various virtual menus by ray-casting from a controller orinputting text through a virtual keyboard to navigate to a desireddestination. The solution presented by the embodiments disclosed hereinto address this challenge may be the XR system may implement voicecommands, combined with one or more other modalities (e.g., gesture,pose, eye gaze, etc.) to allow a user to perform voicenavigation/search, voice FAQ, and voice-driven gameplay & experiences.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includecapturing voice requests with out-of-the-box activation methods. Anothertechnical advantage of the embodiments may include automatic speechrecognition (ASR) for a user's utterance. Another technical advantage ofthe embodiments may include receive live ASR transcription for a spokenutterance. Another technical advantage of the embodiments may includetraining custom NLU capabilities specific for applications. Anothertechnical advantage of the embodiments may include improved voicerequest accuracy with dynamic entities. Certain embodiments disclosedherein may provide none, some, or all of the above technical advantages.One or more other technical advantages may be readily apparent to oneskilled in the art in view of the figures, descriptions, and claims ofthe present disclosure.

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). In particular embodiments, a client system may implement anattention system to provide audio-visual cues to a microphone status inXR environments. There are sometimes issues with users identifying andunderstanding which entities/objects are interactable by voice within anXR environment. Additionally, users sometimes are not knowledgeable onwhat voice commands are available to them for certain contexts, such asfor assistant experiences on voice-forward and voice-only devices andimmersive in-app experiences in XR. To help the user distinguish whichobjects are interactable via voice command, an attention system may beused to provide audio-visual cues that let users know various attentionstates of a XR object, such as when the XR object is ready to receive avoice command, when the microphone is active, and when the system isprocessing the voice command.

In particular embodiments, a client system may be embodied as anaugmented reality headset, virtual reality headset, or extended realityheadset to perform a method of invoking an attention system. Inparticular embodiments, the client system may render, for one or moredisplays of the client system, a first output image of an XR objectwithin an XR environment in a field of view (FOV) of a first user. Inparticular embodiments, the XR object may be interactable by the firstuser. In particular embodiments, the XR object may have a first form. Inparticular embodiments, the client system may detect a change in acontext of the first user with respect to the XR object. In particularembodiments, the client system may determine, based on the detectedchange in the context of the first user, whether to invoke an attentionsystem with respect to the XR object. In particular embodiments, clientsystem may render, for the one or more displays of the client system, asecond output image of the XR object responsive to invoking theattention system. In particular embodiments, the XR object is morphed tohave a second form indicating a first attention state. In particularembodiments, the first attention state indicates a status of the XRobject to interact with one or more first voice commands for one or morefirst functions enabled by the client system. Particular embodiments mayrepeat one or more steps of the method, where appropriate. Although thisdisclosure describes and illustrates particular steps of the method asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method occurring in any suitable order. Moreover,although this disclosure describes and illustrates an example method forinvoking an attention system including the particular steps of themethod, this disclosure contemplates any suitable method for invoking anattention system including any suitable steps, which may include all,some, or none of the steps of the method, where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method, this disclosure contemplates any suitable combination ofany suitable components, devices, or systems carrying out any suitablesteps of the method.

Certain technical challenges exist for understanding a current attentionstate of an object and system. One technical challenge may includeissues with users identifying and understanding which entities/objectsare interactable by voice within an XR environment. The solutionpresented by the embodiments disclosed herein to address this challengemay be providing an attention system that provides an attention state onan object-to-object basis. Another technical challenge may include userssometimes are not knowledgeable on what voice commands are available tothem for certain contexts. The solution presented by the embodimentsdisclosed herein to address this challenge may be to provide a usereducation module to educate the user.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may include helpingthe user distinguish which objects are interactable via voice command,an attention system that may be used to provide audio-visual cues thatlet users know various attention states of a XR object, such as when theXR object is ready to receive a voice command, when the microphone isactive, and when the system is processing the voice command. Anothertechnical advantage of the embodiments may include using visual cues tomap with the attention system states to visually guide users to knowwhen their microphone is on/off and when the attention system is readyto receive voice inputs. Certain embodiments disclosed herein mayprovide none, some, or all of the above technical advantages. One ormore other technical advantages may be readily apparent to one skilledin the art in view of the figures, descriptions, and claims of thepresent disclosure.

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). The client system may use a customized on-devicenatural-language understanding (NLU) models to process voice inputs.Using voice commands in certain XR apps may have issues, such as latencycaused by having to send the audio file to a server, process it, andthen get a response. Even latency of a few hundred microseconds can betoo slow to make certain voice commands usable for particular apps(e.g., most action-based games). As an example and not by way oflimitation, for a fast-paced action-based game, a user may request tocall in an in-game action to be performed in the middle of an in-gameenvironment. The user may expect to have the in-game action be performedwithin a reasonable time period (e.g., a few milliseconds). To reducethe latency of processing voice commands for certain situations, thevoice SDK may enable app developers to add certain voice interactionsonto a customized on-device NLU model for the application so the voicecommands may be executed quicker. The voice SDK may enable the voiceinteractions in applications by using a pattern recognizer thatidentifies certain phrases that are expected for certain situations.

In particular embodiments, one or more client systems may be embodied asan augmented reality headset, virtual reality headset, or extendedreality headset to perform a method of processing an audio input. Inparticular embodiments, one or more clients systems may receive, by oneor more microphones of the one or more client systems, a first audioinput comprising a first voice command of a first plurality of voicecommands associated with a first application. In particular embodiments,the first audio input may not comprise a wake-word. In particularembodiments, the first plurality of voice commands may comprise a firstset of commands executable by a customized on-device natural-languageunderstanding (NLU) model installed on the one or more client systemsand a second set of commands executable by a server-side assistantsystem. In particular embodiments, the one or more client systems mayprocess, using the customized on-device NLU model installed on the oneor more client systems, the first audio input to determine the firstvoice command is associated with the first set of voice commands of thefirst plurality of voice commands. In particular embodiments, the one ormore client systems may execute, by the first application, a first taskcorresponding to the first voice command responsive to the first audioinput. Particular embodiments may repeat one or more steps of themethod, where appropriate. Although this disclosure describes andillustrates particular steps of the method as occurring in a particularorder, this disclosure contemplates any suitable steps of the methodoccurring in any suitable order. Moreover, although this disclosuredescribes and illustrates an example method for processing an audioinput including the particular steps of the method of, this disclosurecontemplates any suitable method for processing an audio input includingany suitable steps, which may include all, some, or none of the steps ofthe method of, where appropriate. Furthermore, although this disclosuredescribes and illustrates particular components, devices, or systemscarrying out particular steps of the method, this disclosurecontemplates any suitable combination of any suitable components,devices, or systems carrying out any suitable steps of the method.

Certain technical challenges exist for processing audio inputs. Onetechnical challenge may include latency caused by having to send theaudio file to a server, process it, and then get a response. Thesolution presented by the embodiments disclosed herein to address thischallenge may be to use a customized on-device NLU model for anapplication so the voice commands may be executed quicker. Anothertechnical challenge may include latency of processing voice commands forcertain situations. The solution presented by the embodiments disclosedherein to address this challenge may be using a pattern recognizer thatidentifies certain phrases that are expected for certain situations.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeenabling voice commands that are executed quickly for applications.Another technical advantage of the embodiments may include customizedNLU model may be trained to bypass the wake word and respond directly toparticular words/commands. Certain embodiments disclosed herein mayprovide none, some, or all of the above technical advantages. One ormore other technical advantages may be readily apparent to one skilledin the art in view of the figures, descriptions, and claims of thepresent disclosure.

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). In particular embodiments, one or more computing systems (e.g.,a server-side assistant system) may customize the tolerance intervalsused by natural-language understanding (NLU) models for processing voiceinteractions via the voice SDK. Developers may have issues initiallyadding voice interactions to their applications. To reduce thecomplexity and improve upon the developer experience, the voice SDK mayinclude voice interactions that allow users to maintain immersion,enable multitasking, and make content more accessible. As an example andnot by way of limitation, the voice SDK may provide interactions thatallow voice-driven gameplay, such as using voice commands to navigate anXR environment. As another example and not by way of limitation, thevoice SDK may provide interactions that allow voice navigation andsearch, such as a voice command to quickly switch a XR environment toanother context (e.g., from one XR generated environment to a new XRgenerated environment) for an application. As another example and not byway of limitation, the voice SDK may provide discovery and usereducation, such as providing an interface to quickly navigate possiblevoice commands available to the user within the application. Voiceinterfaces can unlock entirely new ways of interacting with an XRenvironment. The voice SDK gives developers a way to integrate AI-drivenvoice experiences into XR applications.

In particular embodiments, one or more computing systems embodied asserver-side assistant system may perform the method of tuning aconfidence interval. In particular embodiments, a client system may alsoperform the method of tuning a confidence interval. In particularembodiments, one or more computing systems may receive, from an extendedreality (XR) display device, an audio input of a user of the XR displaydevice. In particular embodiments, the audio input comprises a firstvoice command of a plurality of voice commands associated with a firstapplication. In particular embodiments, the plurality of voice commandsare executable by a natural-language understanding (NLU) model. Inparticular embodiments, the one or more computing systems may determinea first context of the user with respect to an extended reality (XR)environment. In particular embodiments, the one or more computingsystems may determine, based on the first context, a tunable confidenceinterval for the NLU model to match the audio input to the first voicecommand. In particular embodiments, the tunable confidence interval mayvary based on user context. In particular embodiments the tunableconfidence interval may be set to a first tunable confidence level basedon the first context of the user. In particular embodiments, the one ormore computing systems may determine, by the NLU model, whether theaudio input matches the first voice command based on the first tunableconfidence level. In particular embodiments, the one or more computingsystems may execute, by the first application, a first taskcorresponding to the first voice command responsive to determining theaudio input matches the first voice command. Particular embodiments mayrepeat one or more steps of the method, where appropriate. Although thisdisclosure describes and illustrates particular steps of the method asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method occurring in any suitable order. Moreover,although this disclosure describes and illustrates an example method fortuning a confidence interval including the particular steps of themethod, this disclosure contemplates any suitable method for tuning aconfidence interval including any suitable steps, which may include all,some, or none of the steps of the method, where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method, this disclosure contemplates any suitable combination ofany suitable components, devices, or systems carrying out any suitablesteps of the method.

Certain technical challenges exist for voice interactions inapplications. One technical challenge may include maintaining immersionin an application or XR environment. The solution presented by theembodiments disclosed herein to address this challenge may be providinginteractions that allow voice-driven gameplay, such as using voicecommands to navigate an XR environment. Another technical challenge mayinclude the complexity of adding voice interactions to applications. Thesolution presented by the embodiments disclosed herein to address thischallenge may be to use a voice SDK that includes voice interactionsthat allow users to maintain immersion, enable multitasking, and makecontent more accessible.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeproviding interactions that allow voice navigation and search, such as avoice command to quickly switch a XR environment to another context(e.g., from one XR generated environment to a new XR generatedenvironment) for an application. Another technical advantage of theembodiments may include providing discovery and user education, such asproviding an interface to quickly navigate possible voice commandsavailable to the user within the application. Certain embodimentsdisclosed herein may provide none, some, or all of the above technicaladvantages. One or more other technical advantages may be readilyapparent to one skilled in the art in view of the figures, descriptions,and claims of the present disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example flow diagram of the assistant system.

FIG. 4 illustrates an example task-centric flow diagram of processing auser input.

FIG. 5A illustrates an example artificial reality (AR) system.

FIG. 5B illustrates an example augmented reality (AR) system.

FIGS. 6A-6B illustrates an example flow diagram of processing an audioinput.

FIG. 7 illustrates an example flow diagram of processing an audio input.

FIG. 8 illustrates an example architecture of a system to process a userinput.

FIG. 9 illustrates an example method for processing an audio input.

FIG. 10 illustrates an example extended reality (XR) environment of anapplication.

FIG. 11 illustrates an example method for processing a voice command.

FIG. 12 illustrates example indications of the attention state of anobject.

FIG. 13 illustrates an example XR environment containing an attentionsystem.

FIGS. 14A-14B illustrate example user interfaces used for a voicedictionary.

FIG. 15 illustrates an example method for invoking an attention systemof an XR object.

FIG. 16 illustrates an example flow diagram of processing an audioinput.

FIG. 17 illustrates an example XR environment processing an audio inputusing a customized on-device NLU model.

FIG. 18 illustrates an example method for processing an audio input.

FIG. 19 illustrates an example flow diagram for tuning a confidenceinterval.

FIG. 20 illustrates an example method for tuning a confidence interval.

FIG. 21 illustrates an example social graph.

FIG. 22 illustrates an example view of an embedding space.

FIG. 23 illustrates an example artificial neural network.

FIG. 24 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular technology-based network, asatellite communications technology-based network, another network 110,or a combination of two or more such networks 110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be any suitableelectronic device including hardware, software, or embedded logiccomponents, or a combination of two or more such components, and may becapable of carrying out the functionalities implemented or supported bya client system 130. As an example and not by way of limitation, theclient system 130 may include a computer system such as a desktopcomputer, notebook or laptop computer, netbook, a tablet computer,e-book reader, GPS device, camera, personal digital assistant (PDA),handheld electronic device, cellular telephone, smartphone, smartspeaker, smart watch, smart glasses, augmented-reality (AR) smartglasses, virtual reality (VR) headset, other suitable electronic device,or any suitable combination thereof. In particular embodiments, theclient system 130 may be a smart assistant device. More information onsmart assistant devices may be found in U.S. patent application Ser. No.15/949011, filed 9 Apr. 2018, U.S. patent application Ser. No.16/153574, filed 5 Oct. 2018, U.S. Design Patent Application No.29/631910, filed 3 Jan. 2018, U.S. Design Patent Application No.29/631747, filed 2 Jan. 2018, U.S. Design Patent Application No.29/631913, filed 3 Jan. 2018, and U.S. Design Patent Application No.29/631914, filed 3 Jan. 2018, each of which is incorporated byreference. This disclosure contemplates any suitable client systems 130.In particular embodiments, a client system 130 may enable a network userat a client system 130 to access a network 110. The client system 130may also enable the user to communicate with other users at other clientsystems 130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may include an assistant xbotfunctionality as a front-end interface for interacting with the user ofthe client system 130, including receiving user inputs and presentingoutputs. In particular embodiments, the assistant application 136 maycomprise a stand-alone application. In particular embodiments, theassistant application 136 may be integrated into the social-networkingapplication 134 or another suitable application (e.g., a messagingapplication). In particular embodiments, the assistant application 136may be also integrated into the client system 130, an assistant hardwaredevice, or any other suitable hardware devices. In particularembodiments, the assistant application 136 may be also part of theassistant system 140. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may interact with the assistant system 140 byproviding user input to the assistant application 136 via variousmodalities (e.g., audio, voice, text, vision, image, video, gesture,motion, activity, location, orientation). The assistant application 136may communicate the user input to the assistant system 140 (e.g., viathe assistant xbot). Based on the user input, the assistant system 140may generate responses. The assistant system 140 may send the generatedresponses to the assistant application 136. The assistant application136 may then present the responses to the user at the client system 130via various modalities (e.g., audio, text, image, and video). As anexample and not by way of limitation, the user may interact with theassistant system 140 by providing a user input (e.g., a verbal requestfor information regarding a current status of nearby vehicle traffic) tothe assistant xbot via a microphone of the client system 130. Theassistant application 136 may then communicate the user input to theassistant system 140 over network 110. The assistant system 140 mayaccordingly analyze the user input, generate a response based on theanalysis of the user input (e.g., vehicle traffic information obtainedfrom a third-party source), and communicate the generated response backto the assistant application 136. The assistant application 136 may thenpresent the generated response to the user in any suitable manner (e.g.,displaying a text-based push notification and/or image(s) illustrating alocal map of nearby vehicle traffic on a display of the client system130).

In particular embodiments, a client system 130 may implement wake-worddetection techniques to allow users to conveniently activate theassistant system 140 using one or more wake-words associated withassistant system 140. As an example and not by way of limitation, thesystem audio API on client system 130 may continuously monitor userinput comprising audio data (e.g., frames of voice data) received at theclient system 130. In this example, a wake-word associated with theassistant system 140 may be the voice phrase “hey assistant.” In thisexample, when the system audio API on client system 130 detects thevoice phrase “hey assistant” in the monitored audio data, the assistantsystem 140 may be activated for subsequent interaction with the user. Inalternative embodiments, similar detection techniques may be implementedto activate the assistant system 140 using particular non-audio userinputs associated with the assistant system 140. For example, thenon-audio user inputs may be specific visual signals detected by alow-power sensor (e.g., camera) of client system 130. As an example andnot by way of limitation, the visual signals may be a static image(e.g., barcode, QR code, universal product code (UPC)), a position ofthe user (e.g., the user's gaze towards client system 130), a usermotion (e.g., the user pointing at an object), or any other suitablevisual signal.

In particular embodiments, a client system 130 may include a renderingdevice 137 and, optionally, a companion device 138. The rendering device137 may be configured to render outputs generated by the assistantsystem 140 to the user. The companion device 138 may be configured toperform computations associated with particular tasks (e.g.,communications with the assistant system 140) locally (i.e., on-device)on the companion device 138 in particular circumstances (e.g., when therendering device 137 is unable to perform said computations). Inparticular embodiments, the client system 130, the rendering device 137,and/or the companion device 138 may each be a suitable electronic deviceincluding hardware, software, or embedded logic components, or acombination of two or more such components, and may be capable ofcarrying out, individually or cooperatively, the functionalitiesimplemented or supported by the client system 130 described herein. Asan example and not by way of limitation, the client system 130, therendering device 137, and/or the companion device 138 may each include acomputer system such as a desktop computer, notebook or laptop computer,netbook, a tablet computer, e-book reader, GPS device, camera, personaldigital assistant (PDA), handheld electronic device, cellular telephone,smartphone, smart speaker, virtual reality (VR) headset,augmented-reality (AR) smart glasses, XR headset other suitableelectronic device, or any suitable combination thereof. In particularembodiments, one or more of the client system 130, the rendering device137, and the companion device 138 may operate as a smart assistantdevice. As an example and not by way of limitation, the rendering device137 may comprise smart glasses and the companion device 138 may comprisea smart phone. As another example and not by way of limitation, therendering device 137 may comprise a smart watch and the companion device138 may comprise a smart phone. As yet another example and not by way oflimitation, the rendering device 137 may comprise smart glasses and thecompanion device 138 may comprise a smart remote for the smart glasses.As yet another example and not by way of limitation, the renderingdevice 137 may comprise a XR headset and the companion device 138 maycomprise a smart phone.

In particular embodiments, a user may interact with the assistant system140 using the rendering device 137 or the companion device 138,individually or in combination. In particular embodiments, one or moreof the client system 130, the rendering device 137, and the companiondevice 138 may implement a multi-stage wake-word detection model toenable users to conveniently activate the assistant system 140 bycontinuously monitoring for one or more wake-words associated withassistant system 140. At a first stage of the wake-word detection model,the rendering device 137 may receive audio user input (e.g., frames ofvoice data). If a wireless connection between the rendering device 137and the companion device 138 is available, the application on therendering device 137 may communicate the received audio user input tothe companion application on the companion device 138 via the wirelessconnection. At a second stage of the wake-word detection model, thecompanion application on the companion device 138 may process thereceived audio user input to detect a wake-word associated with theassistant system 140. The companion application on the companion device138 may then communicate the detected wake-word to a server associatedwith the assistant system 140 via wireless network 110. At a third stageof the wake-word detection model, the server associated with theassistant system 140 may perform a keyword verification on the detectedwake-word to verify whether the user intended to activate and receiveassistance from the assistant system 140. In alternative embodiments,any of the processing, detection, or keyword verification may beperformed by the rendering device 137 and/or the companion device 138.In particular embodiments, when the assistant system 140 has beenactivated by the user, an application on the rendering device 137 may beconfigured to receive user input from the user, and a companionapplication on the companion device 138 may be configured to handle userinputs (e.g., user requests) received by the application on therendering device 137. In particular embodiments, the rendering device137 and the companion device 138 may be associated with each other(i.e., paired) via one or more wireless communication protocols (e.g.,Bluetooth).

The following example workflow illustrates how a rendering device 137and a companion device 138 may handle a user input provided by a user.In this example, an application on the rendering device 137 may receivea user input comprising a user request directed to the rendering device137. The application on the rendering device 137 may then determine astatus of a wireless connection (i.e., tethering status) between therendering device 137 and the companion device 138. If a wirelessconnection between the rendering device 137 and the companion device 138is not available, the application on the rendering device 137 maycommunicate the user request (optionally including additional dataand/or contextual information available to the rendering device 137) tothe assistant system 140 via the network 110. The assistant system 140may then generate a response to the user request and communicate thegenerated response back to the rendering device 137. The renderingdevice 137 may then present the response to the user in any suitablemanner. Alternatively, if a wireless connection between the renderingdevice 137 and the companion device 138 is available, the application onthe rendering device 137 may communicate the user request (optionallyincluding additional data and/or contextual information available to therendering device 137) to the companion application on the companiondevice 138 via the wireless connection. The companion application on thecompanion device 138 may then communicate the user request (optionallyincluding additional data and/or contextual information available to thecompanion device 138) to the assistant system 140 via the network 110.The assistant system 140 may then generate a response to the userrequest and communicate the generated response back to the companiondevice 138. The companion application on the companion device 138 maythen communicate the generated response to the application on therendering device 137. The rendering device 137 may then present theresponse to the user in any suitable manner. In the preceding exampleworkflow, the rendering device 137 and the companion device 138 may eachperform one or more computations and/or processes at each respectivestep of the workflow. In particular embodiments, performance of thecomputations and/or processes disclosed herein may be adaptivelyswitched between the rendering device 137 and the companion device 138based at least in part on a device state of the rendering device 137and/or the companion device 138, a task associated with the user input,and/or one or more additional factors. As an example and not by way oflimitation, one factor may be signal strength of the wireless connectionbetween the rendering device 137 and the companion device 138. Forexample, if the signal strength of the wireless connection between therendering device 137 and the companion device 138 is strong, thecomputations and processes may be adaptively switched to besubstantially performed by the companion device 138 in order to, forexample, benefit from the greater processing power of the CPU of thecompanion device 138. Alternatively, if the signal strength of thewireless connection between the rendering device 137 and the companiondevice 138 is weak, the computations and processes may be adaptivelyswitched to be substantially performed by the rendering device 137 in astandalone manner. In particular embodiments, if the client system 130does not comprise a companion device 138, the aforementionedcomputations and processes may be performed solely by the renderingdevice 137 in a standalone manner.

In particular embodiments, an assistant system 140 may assist users withvarious assistant-related tasks. The assistant system 140 may interactwith the social-networking system 160 and/or the third-party system 170when executing these assistant-related tasks.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132 or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.As an example and not by way of limitation, each server 162 may be a webserver, a news server, a mail server, a message server, an advertisingserver, a file server, an application server, an exchange server, adatabase server, a proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, an assistant system 140, or a third-party system 170 tomanage, retrieve, modify, add, or delete, the information stored in datastore 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects. In particularembodiments, a third-party content provider may use one or morethird-party agents to provide content objects and/or services. Athird-party agent may be an implementation that is hosted and executingon the third-party system 170.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow, forexample, an assistant system 140 or a third-party system 170 to accessinformation from the social-networking system 160 by calling one or moreAPIs. An action logger may be used to receive communications from a webserver about a user's actions on or off the social-networking system160. In conjunction with the action log, a third-party-content-objectlog may be maintained of user exposures to third-party-content objects.A notification controller may provide information regarding contentobjects to a client system 130. Information may be pushed to a clientsystem 130 as notifications, or information may be pulled from a clientsystem 130 responsive to a user input comprising a user request receivedfrom a client system 130. Authorization servers may be used to enforceone or more privacy settings of the users of the social-networkingsystem 160. A privacy setting of a user may determine how particularinformation associated with a user can be shared. The authorizationserver may allow users to opt in to or opt out of having their actionslogged by the social-networking system 160 or shared with other systems(e.g., a third-party system 170), such as, for example, by settingappropriate privacy settings. Third-party-content-object stores may beused to store content objects received from third parties, such as athird-party system 170. Location stores may be used for storing locationinformation received from client systems 130 associated with users.Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Assistant Systems

FIG. 2 illustrates an example architecture 200 of the assistant system140. In particular embodiments, the assistant system 140 may assist auser to obtain information or services. The assistant system 140 mayenable the user to interact with the assistant system 140 via userinputs of various modalities (e.g., audio, voice, text, vision, image,video, gesture, motion, activity, location, orientation) in stateful andmulti-turn conversations to receive assistance from the assistant system140. As an example and not by way of limitation, a user input maycomprise an audio input based on the user's voice (e.g., a verbalcommand), which may be processed by a system audio API (applicationprogramming interface) on client system 130. The system audio API mayperform techniques including echo cancellation, noise removal, beamforming, self-user voice activation, speaker identification, voiceactivity detection (VAD), and/or any other suitable acoustic techniquein order to generate audio data that is readily processable by theassistant system 140. In particular embodiments, the assistant system140 may support mono-modal inputs (e.g., only voice inputs), multi-modalinputs (e.g., voice inputs and text inputs), hybrid/multi-modal inputs,or any combination thereof. In particular embodiments, a user input maybe a user-generated input that is sent to the assistant system 140 in asingle turn. User inputs provided by a user may be associated withparticular assistant-related tasks, and may include, for example, userrequests (e.g., verbal requests for information or performance of anaction), user interactions with the assistant application 136 associatedwith the assistant system 140 (e.g., selection of UI elements via touchor gesture), or any other type of suitable user input that may bedetected and understood by the assistant system 140 (e.g., usermovements detected by the client device 130 of the user).

In particular embodiments, the assistant system 140 may create and storea user profile comprising both personal and contextual informationassociated with the user. In particular embodiments, the assistantsystem 140 may analyze the user input using natural-languageunderstanding (NLU) techniques. The analysis may be based at least inpart on the user profile of the user for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation (NLG). Through the interaction with theuser, the assistant system 140 may use dialog management techniques tomanage and forward the conversation flow with the user. In particularembodiments, the assistant system 140 may further assist the user toeffectively and efficiently digest the obtained information bysummarizing the information. The assistant system 140 may also assistthe user to be more engaging with an online social network by providingtools that help the user interact with the online social network (e.g.,creating posts, comments, messages). The assistant system 140 mayadditionally assist the user to manage different tasks such as keepingtrack of events. In particular embodiments, the assistant system 140 mayproactively execute, without a user input, pre-authorized tasks that arerelevant to user interests and preferences based on the user profile, ata time relevant for the user. In particular embodiments, the assistantsystem 140 may check privacy settings to ensure that accessing a user'sprofile or other user information and executing different tasks arepermitted subject to the user's privacy settings. More information onassisting users subject to privacy settings may be found in Moreinformation on assisting users subject to privacy settings may be foundin U.S. patent application Ser. No. 16/182542, filed 6 Nov. 2018, whichis incorporated by reference.

In particular embodiments, the assistant system 140 may assist a uservia an architecture built upon client-side processes and server-sideprocesses which may operate in various operational modes. In FIG. 2 ,the client-side process is illustrated above the dashed line 202 whereasthe server-side process is illustrated below the dashed line 202. Afirst operational mode (i.e., on-device mode) may be a workflow in whichthe assistant system 140 processes a user input and provides assistanceto the user by primarily or exclusively performing client-side processeslocally on the client system 130. For example, if the client system 130is not connected to a network 110 (i.e., when client system 130 isoffline), the assistant system 140 may handle a user input in the firstoperational mode utilizing only client-side processes. A secondoperational mode (i.e., cloud mode) may be a workflow in which theassistant system 140 processes a user input and provides assistance tothe user by primarily or exclusively performing server-side processes onone or more remote servers (e.g., a server associated with assistantsystem 140). As illustrated in FIG. 2 , a third operational mode (i.e.,blended mode) may be a parallel workflow in which the assistant system140 processes a user input and provides assistance to the user byperforming client-side processes locally on the client system 130 inconjunction with server-side processes on one or more remote servers(e.g., a server associated with assistant system 140). For example, theclient system 130 and the server associated with assistant system 140may both perform automatic speech recognition (ASR) and natural-languageunderstanding (NLU) processes, but the client system 130 may delegatedialog, agent, and natural-language generation (NLG) processes to beperformed by the server associated with assistant system 140.

In particular embodiments, selection of an operational mode may be basedat least in part on a device state, a task associated with a user input,and/or one or more additional factors. As an example and not by way oflimitation, as described above, one factor may be a network connectivitystatus for client system 130. For example, if the client system 130 isnot connected to a network 110 (i.e., when client system 130 isoffline), the assistant system 140 may handle a user input in the firstoperational mode (i.e., on-device mode). As another example and not byway of limitation, another factor may be based on a measure of availablebattery power (i.e., battery status) for the client system 130. Forexample, if there is a need for client system 130 to conserve batterypower (e.g., when client system 130 has minimal available battery poweror the user has indicated a desire to conserve the battery power of theclient system 130), the assistant system 140 may handle a user input inthe second operational mode (i.e., cloud mode) or the third operationalmode (i.e., blended mode) in order to perform fewer power-intensiveoperations on the client system 130. As yet another example and not byway of limitation, another factor may be one or more privacy constraints(e.g., specified privacy settings, applicable privacy policies). Forexample, if one or more privacy constraints limits or precludesparticular data from being transmitted to a remote server (e.g., aserver associated with the assistant system 140), the assistant system140 may handle a user input in the first operational mode (i.e.,on-device mode) in order to protect user privacy. As yet another exampleand not by way of limitation, another factor may be desynchronizedcontext data between the client system 130 and a remote server (e.g.,the server associated with assistant system 140). For example, theclient system 130 and the server associated with assistant system 140may be determined to have inconsistent, missing, and/or unreconciledcontext data, the assistant system 140 may handle a user input in thethird operational mode (i.e., blended mode) to reduce the likelihood ofan inadequate analysis associated with the user input. As yet anotherexample and not by way of limitation, another factor may be a measure oflatency for the connection between client system 130 and a remote server(e.g., the server associated with assistant system 140). For example, ifa task associated with a user input may significantly benefit fromand/or require prompt or immediate execution (e.g., photo capturingtasks), the assistant system 140 may handle the user input in the firstoperational mode (i.e., on-device mode) to ensure the task is performedin a timely manner. As yet another example and not by way of limitation,another factor may be, for a feature relevant to a task associated witha user input, whether the feature is only supported by a remote server(e.g., the server associated with assistant system 140). For example, ifthe relevant feature requires advanced technical functionality (e.g.,high-powered processing capabilities, rapid update cycles) that is onlysupported by the server associated with assistant system 140 and is notsupported by client system 130 at the time of the user input, theassistant system 140 may handle the user input in the second operationalmode (i.e., cloud mode) or the third operational mode (i.e., blendedmode) in order to benefit from the relevant feature.

In particular embodiments, an on-device orchestrator 206 on the clientsystem 130 may coordinate receiving a user input and may determine, atone or more decision points in an example workflow, which of theoperational modes described above should be used to process or continueprocessing the user input. As discussed above, selection of anoperational mode may be based at least in part on a device state, a taskassociated with a user input, and/or one or more additional factors. Asan example and not by way of limitation, with reference to the workflowarchitecture illustrated in FIG. 2 , after a user input is received froma user, the on-device orchestrator 206 may determine, at decision point(DO) 205, whether to begin processing the user input in the firstoperational mode (i.e., on-device mode), the second operational mode(i.e., cloud mode), or the third operational mode (i.e., blended mode).For example, at decision point (DO) 205, the on-device orchestrator 206may select the first operational mode (i.e., on-device mode) if theclient system 130 is not connected to network 110 (i.e., when clientsystem 130 is offline), if one or more privacy constraints expresslyrequire on-device processing (e.g., adding or removing another person toa private call between users), or if the user input is associated with atask which does not require or benefit from server-side processing(e.g., setting an alarm or calling another user). As another example, atdecision point (DO) 205, the on-device orchestrator 206 may select thesecond operational mode (i.e., cloud mode) or the third operational mode(i.e., blended mode) if the client system 130 has a need to conservebattery power (e.g., when client system 130 has minimal availablebattery power or the user has indicated a desire to conserve the batterypower of the client system 130) or has a need to limit additionalutilization of computing resources (e.g., when other processes operatingon client device 130 require high CPU utilization (e.g., SMS messagingapplications)).

In particular embodiments, if the on-device orchestrator 206 determinesat decision point (DO) 205 that the user input should be processed usingthe first operational mode (i.e., on-device mode) or the thirdoperational mode (i.e., blended mode), the client-side process maycontinue as illustrated in FIG. 2 . As an example and not by way oflimitation, if the user input comprises speech data, the speech data maybe received at a local automatic speech recognition (ASR) module 208 aon the client system 130. The ASR module 208 a may allow a user todictate and have speech transcribed as written text, have a documentsynthesized as an audio stream, or issue commands that are recognized assuch by the system.

In particular embodiments, the output of the ASR module 208 a may besent to a local natural-language understanding (NLU) module 210 a. TheNLU module 210 a may perform named entity resolution (NER), or namedentity resolution may be performed by the entity resolution module 212a, as described below. In particular embodiments, one or more of anintent, a slot, or a domain may be an output of the NLU module 210 a.

In particular embodiments, the user input may comprise non-speech data,which may be received at a local context engine 220 a. As an example andnot by way of limitation, the non-speech data may comprise locations,visuals, touch, gestures, world updates, social updates, contextualinformation, information related to people, activity data, and/or anyother suitable type of non-speech data. The non-speech data may furthercomprise sensory data received by client system 130 sensors (e.g.,microphone, camera), which may be accessed subject to privacyconstraints and further analyzed by computer vision technologies. Inparticular embodiments, the computer vision technologies may compriseobject detection, scene recognition, hand tracking, eye tracking, and/orany other suitable computer vision technologies. In particularembodiments, the non-speech data may be subject to geometricconstructions, which may comprise constructing objects surrounding auser using any suitable type of data collected by a client system 130.As an example and not by way of limitation, a user may be wearing ARglasses, and geometric constructions may be utilized to determinespatial locations of surfaces and items (e.g., a floor, a wall, a user'shands). In particular embodiments, the non-speech data may be inertialdata captured by AR glasses or a VR headset or XR headset, and which maybe data associated with linear and angular motions (e.g., measurementsassociated with a user's body movements). In particular embodiments, thecontext engine 220 a may determine various types of events and contextbased on the non-speech data.

In particular embodiments, the outputs of the NLU module 210 a and/orthe context engine 220 a may be sent to an entity resolution module 212a. The entity resolution module 212 a may resolve entities associatedwith one or more slots output by NLU module 210 a. In particularembodiments, each resolved entity may be associated with one or moreentity identifiers. As an example and not by way of limitation, anidentifier may comprise a unique user identifier (ID) corresponding to aparticular user (e.g., a unique username or user ID number for thesocial-networking system 160). In particular embodiments, each resolvedentity may also be associated with a confidence score. More informationon resolving entities may be found in U.S. Pat. No. 10,803,050, filed 27Jul. 2018, and U.S. patent application Ser. No. 16/048072, filed 27 Jul.2018, each of which is incorporated by reference.

In particular embodiments, at decision point (DO) 205, the on-deviceorchestrator 206 may determine that a user input should be handled inthe second operational mode (i.e., cloud mode) or the third operationalmode (i.e., blended mode). In these operational modes, the user inputmay be handled by certain server-side modules in a similar manner as theclient-side process described above.

In particular embodiments, if the user input comprises speech data, thespeech data of the user input may be received at a remote automaticspeech recognition (ASR) module 208 b on a remote server (e.g., theserver associated with assistant system 140). The ASR module 208 b mayallow a user to dictate and have speech transcribed as written text,have a document synthesized as an audio stream, or issue commands thatare recognized as such by the system.

In particular embodiments, the output of the ASR module 208 b may besent to a remote natural-language understanding (NLU) module 210 b. Inparticular embodiments, the NLU module 210 b may perform named entityresolution (NER) or named entity resolution may be performed by entityresolution module 212 b of dialog manager module 216 b as describedbelow. In particular embodiments, one or more of an intent, a slot, or adomain may be an output of the NLU module 210 b.

In particular embodiments, the user input may comprise non-speech data,which may be received at a remote context engine 220 b. In particularembodiments, the remote context engine 220 b may determine various typesof events and context based on the non-speech data. In particularembodiments, the output of the NLU module 210 b and/or the contextengine 220 b may be sent to a remote dialog manager 216 b.

In particular embodiments, as discussed above, an on-device orchestrator206 on the client system 130 may coordinate receiving a user input andmay determine, at one or more decision points in an example workflow,which of the operational modes described above should be used to processor continue processing the user input. As further discussed above,selection of an operational mode may be based at least in part on adevice state, a task associated with a user input, and/or one or moreadditional factors. As an example and not by way of limitation, withcontinued reference to the workflow architecture illustrated in FIG. 2 ,after the entity resolution module 212 a generates an output or a nulloutput, the on-device orchestrator 206 may determine, at decision point(D1) 215, whether to continue processing the user input in the firstoperational mode (i.e., on-device mode), the second operational mode(i.e., cloud mode), or the third operational mode (i.e., blended mode).For example, at decision point (D1) 215, the on-device orchestrator 206may select the first operational mode (i.e., on-device mode) if anidentified intent is associated with a latency sensitive processing task(e.g., taking a photo, pausing a stopwatch). As another example and notby way of limitation, if a messaging task is not supported by on-deviceprocessing on the client system 130, the on-device orchestrator 206 mayselect the third operational mode (i.e., blended mode) to process theuser input associated with a messaging request. As yet another example,at decision point (D1) 215, the on-device orchestrator 206 may selectthe second operational mode (i.e., cloud mode) or the third operationalmode (i.e., blended mode) if the task being processed requires access toa social graph, a knowledge graph, or a concept graph not stored on theclient system 130. Alternatively, the on-device orchestrator 206 mayinstead select the first operational mode (i.e., on-device mode) if asufficient version of an informational graph including requisiteinformation for the task exists on the client system 130 (e.g., asmaller and/or bootstrapped version of a knowledge graph).

In particular embodiments, if the on-device orchestrator 206 determinesat decision point (D1) 215 that processing should continue using thefirst operational mode (i.e., on-device mode) or the third operationalmode (i.e., blended mode), the client-side process may continue asillustrated in FIG. 2 . As an example and not by way of limitation, theoutput from the entity resolution module 212 a may be sent to anon-device dialog manager 216 a. In particular embodiments, the on-devicedialog manager 216 a may comprise a dialog state tracker 218 a and anaction selector 222 a. The on-device dialog manager 216 a may havecomplex dialog logic and product-related business logic to manage thedialog state and flow of the conversation between the user and theassistant system 140. The on-device dialog manager 216 a may includefull functionality for end-to-end integration and multi-turn support(e.g., confirmation, disambiguation). The on-device dialog manager 216 amay also be lightweight with respect to computing limitations andresources including memory, computation (CPU), and binary sizeconstraints. The on-device dialog manager 216 a may also be scalable toimprove developer experience. In particular embodiments, the on-devicedialog manager 216 a may benefit the assistant system 140, for example,by providing offline support to alleviate network connectivity issues(e.g., unstable or unavailable network connections), by usingclient-side processes to prevent privacy-sensitive information frombeing transmitted off of client system 130, and by providing a stableuser experience in high-latency sensitive scenarios.

In particular embodiments, the on-device dialog manager 216 a mayfurther conduct false trigger mitigation. Implementation of falsetrigger mitigation may detect and prevent false triggers from userinputs which would otherwise invoke the assistant system 140 (e.g., anunintended wake-word) and may further prevent the assistant system 140from generating data records based on the false trigger that may beinaccurate and/or subject to privacy constraints. As an example and notby way of limitation, if a user is in a voice call, the user'sconversation during the voice call may be considered private, and thefalse trigger mitigation may limit detection of wake-words to audio userinputs received locally by the user's client system 130. In particularembodiments, the on-device dialog manager 216 a may implement falsetrigger mitigation based on a nonsense detector. If the nonsensedetector determines with a high confidence that a received wake-word isnot logically and/or contextually sensible at the point in time at whichit was received from the user, the on-device dialog manager 216 a maydetermine that the user did not intend to invoke the assistant system140.

In particular embodiments, due to a limited computing power of theclient system 130, the on-device dialog manager 216 a may conducton-device learning based on learning algorithms particularly tailoredfor client system 130. As an example and not by way of limitation,federated learning techniques may be implemented by the on-device dialogmanager 216 a. Federated learning is a specific category of distributedmachine learning techniques which may train machine-learning modelsusing decentralized data stored on end devices (e.g., mobile phones). Inparticular embodiments, the on-device dialog manager 216 a may usefederated user representation learning model to extend existingneural-network personalization techniques to implementation of federatedlearning by the on-device dialog manager 216 a. Federated userrepresentation learning may personalize federated learning models bylearning task-specific user representations (i.e., embeddings) and/or bypersonalizing model weights. Federated user representation learning is asimple, scalable, privacy-preserving, and resource-efficient. Federateduser representation learning may divide model parameters into federatedand private parameters. Private parameters, such as private userembeddings, may be trained locally on a client system 130 instead ofbeing transferred to or averaged by a remote server (e.g., the serverassociated with assistant system 140). Federated parameters, bycontrast, may be trained remotely on the server. In particularembodiments, the on-device dialog manager 216 a may use an activefederated learning model, which may transmit a global model trained onthe remote server to client systems 130 and calculate gradients locallyon the client systems 130. Active federated learning may enable theon-device dialog manager 216 a to minimize the transmission costsassociated with downloading models and uploading gradients. For activefederated learning, in each round, client systems 130 may be selected ina semi-random manner based at least in part on a probability conditionedon the current model and the data on the client systems 130 in order tooptimize efficiency for training the federated learning model.

In particular embodiments, the dialog state tracker 218 a may trackstate changes over time as a user interacts with the world and theassistant system 140 interacts with the user. As an example and not byway of limitation, the dialog state tracker 218 a may track, forexample, what the user is talking about, whom the user is with, wherethe user is, what tasks are currently in progress, and where the user'sgaze is at subject to applicable privacy policies.

In particular embodiments, at decision point (D1) 215, the on-deviceorchestrator 206 may determine to forward the user input to the serverfor either the second operational mode (i.e., cloud mode) or the thirdoperational mode (i.e., blended mode). As an example and not by way oflimitation, if particular functionalities or processes (e.g., messaging)are not supported by on the client system 130, the on-deviceorchestrator 206 may determine at decision point (D1) 215 to use thethird operational mode (i.e., blended mode). In particular embodiments,the on-device orchestrator 206 may cause the outputs from the NLU module210 a, the context engine 220 a, and the entity resolution module 212 a,via a dialog manager proxy 224, to be forwarded to an entity resolutionmodule 212 b of the remote dialog manager 216 b to continue theprocessing. The dialog manager proxy 224 may be a communication channelfor information/events exchange between the client system 130 and theserver. In particular embodiments, the dialog manager 216 b mayadditionally comprise a remote arbitrator 226 b, a remote dialog statetracker 218 b, and a remote action selector 222 b. In particularembodiments, the assistant system 140 may have started processing a userinput with the second operational mode (i.e., cloud mode) at decisionpoint (DO) 205 and the on-device orchestrator 206 may determine tocontinue processing the user input based on the second operational mode(i.e., cloud mode) at decision point (D1) 215. Accordingly, the outputfrom the NLU module 210 b and the context engine 220 b may be receivedat the remote entity resolution module 212 b. The remote entityresolution module 212 b may have similar functionality as the localentity resolution module 212 a, which may comprise resolving entitiesassociated with the slots. In particular embodiments, the entityresolution module 212 b may access one or more of the social graph, theknowledge graph, or the concept graph when resolving the entities. Theoutput from the entity resolution module 212 b may be received at thearbitrator 226 b.

In particular embodiments, the remote arbitrator 226 b may beresponsible for choosing between client-side and server-side upstreamresults (e.g., results from the NLU module 210 a/b, results from theentity resolution module 212 a/b, and results from the context engine220 a/b). The arbitrator 226 b may send the selected upstream results tothe remote dialog state tracker 218 b. In particular embodiments,similarly to the local dialog state tracker 218 a, the remote dialogstate tracker 218 b may convert the upstream results into candidatetasks using task specifications and resolve arguments with entityresolution.

In particular embodiments, at decision point (D2) 225, the on-deviceorchestrator 206 may determine whether to continue processing the userinput based on the first operational mode (i.e., on-device mode) orforward the user input to the server for the third operational mode(i.e., blended mode). The decision may depend on, for example, whetherthe client-side process is able to resolve the task and slotssuccessfully, whether there is a valid task policy with a specificfeature support, and/or the context differences between the client-sideprocess and the server-side process. In particular embodiments,decisions made at decision point (D2) 225 may be for multi-turnscenarios. In particular embodiments, there may be at least two possiblescenarios. In a first scenario, the assistant system 140 may havestarted processing a user input in the first operational mode (i.e.,on-device mode) using client-side dialog state. If at some point theassistant system 140 decides to switch to having the remote serverprocess the user input, the assistant system 140 may create aprogrammatic/predefined task with the current task state and forward itto the remote server. For subsequent turns, the assistant system 140 maycontinue processing in the third operational mode (i.e., blended mode)using the server-side dialog state. In another scenario, the assistantsystem 140 may have started processing the user input in either thesecond operational mode (i.e., cloud mode) or the third operational mode(i.e., blended mode) and may substantially rely on server-side dialogstate for all subsequent turns. If the on-device orchestrator 206determines to continue processing the user input based on the firstoperational mode (i.e., on-device mode), the output from the dialogstate tracker 218 a may be received at the action selector 222 a.

In particular embodiments, at decision point (D2) 225, the on-deviceorchestrator 206 may determine to forward the user input to the remoteserver and continue processing the user input in either the secondoperational mode (i.e., cloud mode) or the third operational mode (i.e.,blended mode). The assistant system 140 may create aprogrammatic/predefined task with the current task state and forward itto the server, which may be received at the action selector 222 b. Inparticular embodiments, the assistant system 140 may have startedprocessing the user input in the second operational mode (i.e., cloudmode), and the on-device orchestrator 206 may determine to continueprocessing the user input in the second operational mode (i.e., cloudmode) at decision point (D2) 225. Accordingly, the output from thedialog state tracker 218 b may be received at the action selector 222 b.

In particular embodiments, the action selector 222 a/b may performinteraction management. The action selector 222 a/b may determine andtrigger a set of general executable actions. The actions may be executedeither on the client system 130 or at the remote server. As an exampleand not by way of limitation, these actions may include providinginformation or suggestions to the user. In particular embodiments, theactions may interact with agents 228 a/b, users, and/or the assistantsystem 140 itself. These actions may comprise actions including one ormore of a slot request, a confirmation, a disambiguation, or an agentexecution. The actions may be independent of the underlyingimplementation of the action selector 222 a/b. For more complicatedscenarios such as, for example, multi-turn tasks or tasks with complexbusiness logic, the local action selector 222 a may call one or morelocal agents 228 a, and the remote action selector 222 b may call one ormore remote agents 228 b to execute the actions. Agents 228 a/b may beinvoked via task ID, and any actions may be routed to the correct agent228 a/b using that task ID. In particular embodiments, an agent 228 a/bmay be configured to serve as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. In particular embodiments,agents 228 a/b may provide several functionalities for the assistantsystem 140 including, for example, native template generation, taskspecific business logic, and querying external APIs. When executingactions for a task, agents 228 a/b may use context from the dialog statetracker 218 a/b, and may also update the dialog state tracker 218 a/b.In particular embodiments, agents 228 a/b may also generate partialpayloads from a dialog act.

In particular embodiments, the local agents 228 a may have differentimplementations to be compiled/registered for different platforms (e.g.,smart glasses versus a VR headset or a XR headset). In particularembodiments, multiple device-specific implementations (e.g., real-timecalls for a client system 130 or a messaging application on the clientsystem 130) may be handled internally by a single agent 228 a.Alternatively, device-specific implementations may be handled bymultiple agents 228 a associated with multiple domains. As an exampleand not by way of limitation, calling an agent 228 a on smart glassesmay be implemented in a different manner than calling an agent 228 a ona smart phone. Different platforms may also utilize varying numbers ofagents 228 a. The agents 228 a may also be cross-platform (i.e.,different operating systems on the client system 130). In addition, theagents 228 a may have minimized startup time or binary size impact.Local agents 228 a may be suitable for particular use cases. As anexample and not by way of limitation, one use case may be emergencycalling on the client system 130. As another example and not by way oflimitation, another use case may be responding to a user input withoutnetwork connectivity. As yet another example and not by way oflimitation, another use case may be that particular domains/tasks may beprivacy sensitive and may prohibit user inputs being sent to the remoteserver.

In particular embodiments, the local action selector 222 a may call alocal delivery system 230 a for executing the actions, and the remoteaction selector 222 b may call a remote delivery system 230 b forexecuting the actions. The delivery system 230 a/b may deliver apredefined event upon receiving triggering signals from the dialog statetracker 218 a/b by executing corresponding actions. The delivery system230 a/b may ensure that events get delivered to a host with a livingconnection. As an example and not by way of limitation, the deliverysystem 230 a/b may broadcast to all online devices that belong to oneuser. As another example and not by way of limitation, the deliverysystem 230 a/b may deliver events to target-specific devices. Thedelivery system 230 a/b may further render a payload using up-to-datedevice context.

In particular embodiments, the on-device dialog manager 216 a mayadditionally comprise a separate local action execution module, and theremote dialog manager 216 b may additionally comprise a separate remoteaction execution module. The local execution module and the remoteaction execution module may have similar functionality. In particularembodiments, the action execution module may call the agents 228 a/b toexecute tasks. The action execution module may additionally perform aset of general executable actions determined by the action selector 222a/b. The set of executable actions may interact with agents 228 a/b,users, and the assistant system 140 itself via the delivery system 230a/b.

In particular embodiments, if the user input is handled using the firstoperational mode (i.e., on-device mode), results from the agents 228 aand/or the delivery system 230 a may be returned to the on-device dialogmanager 216 a. The on-device dialog manager 216 a may then instruct alocal arbitrator 226 a to generate a final response based on theseresults. The arbitrator 226 a may aggregate the results and evaluatethem. As an example and not by way of limitation, the arbitrator 226 amay rank and select a best result for responding to the user input. Ifthe user request is handled in the second operational mode (i.e., cloudmode), the results from the agents 228 b and/or the delivery system 230b may be returned to the remote dialog manager 216 b. The remote dialogmanager 216 b may instruct, via the dialog manager proxy 224, thearbitrator 226 a to generate the final response based on these results.Similarly, the arbitrator 226 a may analyze the results and select thebest result to provide to the user. If the user input is handled basedon the third operational mode (i.e., blended mode), the client-sideresults and server-side results (e.g., from agents 228 a/b and/ordelivery system 230 a/b) may both be provided to the arbitrator 226 a bythe on-device dialog manager 216 a and remote dialog manager 216 b,respectively. The arbitrator 226 may then choose between the client-sideand server-side side results to determine the final result to bepresented to the user. In particular embodiments, the logic to decidebetween these results may depend on the specific use-case.

In particular embodiments, the local arbitrator 226 a may generate aresponse based on the final result and send it to a render output module232. The render output module 232 may determine how to render the outputin a way that is suitable for the client system 130. As an example andnot by way of limitation, for a XR headset or VR headset or AR smartglasses, the render output module 232 may determine to render the outputusing a visual-based modality (e.g., an image or a video clip) that maybe displayed via the XR headset or VR headset or AR smart glasses. Asanother example, the response may be rendered as audio signals that maybe played by the user via a XR headset or VR headset or AR smartglasses. As yet another example, the response may be rendered asaugmented-reality data for enhancing user experience.

In particular embodiments, in addition to determining an operationalmode to process the user input, the on-device orchestrator 206 may alsodetermine whether to process the user input on the rendering device 137,process the user input on the companion device 138, or process the userrequest on the remote server. The rendering device 137 and/or thecompanion device 138 may each use the assistant stack in a similarmanner as disclosed above to process the user input. As an example andnot by, the on-device orchestrator 206 may determine that part of theprocessing should be done on the rendering device 137, part of theprocessing should be done on the companion device 138, and the remainingprocessing should be done on the remote server.

In particular embodiments, the assistant system 140 may have a varietyof capabilities including audio cognition, visual cognition, signalsintelligence, reasoning, and memories. In particular embodiments, thecapability of audio cognition may enable the assistant system 140 to,for example, understand a user's input associated with various domainsin different languages, understand and summarize a conversation, performon-device audio cognition for complex commands, identify a user byvoice, extract topics from a conversation and auto-tag sections of theconversation, enable audio interaction without a wake-word, filter andamplify user voice from ambient noise and conversations, and/orunderstand which client system 130 a user is talking to if multipleclient systems 130 are in vicinity.

In particular embodiments, the capability of visual cognition may enablethe assistant system 140 to, for example, recognize interesting objectsin the world through a combination of existing machine-learning modelsand one-shot learning, recognize an interesting moment and auto-captureit, achieve semantic understanding over multiple visual frames acrossdifferent episodes of time, provide platform support for additionalcapabilities in places or objects recognition, recognize a full set ofsettings and micro-locations including personalized locations, recognizecomplex activities, recognize complex gestures to control a clientsystem 130, handle images/videos from egocentric cameras (e.g., withmotion, capture angles, resolution), accomplish similar levels ofaccuracy and speed regarding images with lower resolution, conductone-shot registration and recognition of places and objects, and/orperform visual recognition on a client system 130.

In particular embodiments, the assistant system 140 may leveragecomputer vision techniques to achieve visual cognition. Besides computervision techniques, the assistant system 140 may explore options that maysupplement these techniques to scale up the recognition of objects. Inparticular embodiments, the assistant system 140 may use supplementalsignals such as, for example, optical character recognition (OCR) of anobject's labels, GPS signals for places recognition, and/or signals froma user's client system 130 to identify the user. In particularembodiments, the assistant system 140 may perform general scenerecognition (e.g., home, work, public spaces) to set a context for theuser and reduce the computer-vision search space to identify likelyobjects or people. In particular embodiments, the assistant system 140may guide users to train the assistant system 140. For example,crowdsourcing may be used to get users to tag objects and help theassistant system 140 recognize more objects over time. As anotherexample, users may register their personal objects as part of an initialsetup when using the assistant system 140. The assistant system 140 mayfurther allow users to provide positive/negative signals for objectsthey interact with to train and improve personalized models for them.

In particular embodiments, the capability of signals intelligence mayenable the assistant system 140 to, for example, determine userlocation, understand date/time, determine family locations, understandusers' calendars and future desired locations, integrate richer soundunderstanding to identify setting/context through sound alone, and/orbuild signals intelligence models at runtime which may be personalizedto a user's individual routines.

In particular embodiments, the capability of reasoning may enable theassistant system 140 to, for example, pick up previous conversationthreads at any point in the future, synthesize all signals to understandmicro and personalized context, learn interaction patterns andpreferences from users' historical behavior and accurately suggestinteractions that they may value, generate highly predictive proactivesuggestions based on micro-context understanding, understand whatcontent a user may want to see at what time of a day, and/or understandthe changes in a scene and how that may impact the user's desiredcontent.

In particular embodiments, the capabilities of memories may enable theassistant system 140 to, for example, remember which social connectionsa user previously called or interacted with, write into memory and querymemory at will (i.e., open dictation and auto tags), extract richerpreferences based on prior interactions and long-term learning, remembera user's life history, extract rich information from egocentric streamsof data and auto catalog, and/or write to memory in structured form toform rich short, episodic and long-term memories.

FIG. 3 illustrates an example flow diagram 300 of the assistant system140. In particular embodiments, an assistant service module 305 mayaccess a request manager 310 upon receiving a user input. In particularembodiments, the request manager 310 may comprise a context extractor312 and a conversational understanding object generator (CU objectgenerator) 314. The context extractor 312 may extract contextualinformation associated with the user input. The context extractor 312may also update contextual information based on the assistantapplication 136 executing on the client system 130. As an example andnot by way of limitation, the update of contextual information maycomprise content items are displayed on the client system 130. Asanother example and not by way of limitation, the update of contextualinformation may comprise whether an alarm is set on the client system130. As another example and not by way of limitation, the update ofcontextual information may comprise whether a song is playing on theclient system 130. The CU object generator 314 may generate particularCU objects relevant to the user input. The CU objects may comprisedialog-session data and features associated with the user input, whichmay be shared with all the modules of the assistant system 140. Inparticular embodiments, the request manager 310 may store the contextualinformation and the generated CU objects in a data store 320 which is aparticular data store implemented in the assistant system 140.

In particular embodiments, the request manger 310 may send the generatedCU objects to the NLU module 210. The NLU module 210 may perform aplurality of steps to process the CU objects. The NLU module 210 mayfirst run the CU objects through an allowlist/blocklist 330. Inparticular embodiments, the allowlist/blocklist 330 may compriseinterpretation data matching the user input. The NLU module 210 may thenperform a featurization 332 of the CU objects. The NLU module 210 maythen perform domain classification/selection 334 on user input based onthe features resulted from the featurization 332 to classify the userinput into predefined domains. In particular embodiments, a domain maydenote a social context of interaction (e.g., education), or a namespacefor a set of intents (e.g., music). The domain classification/selectionresults may be further processed based on two related procedures. In oneprocedure, the NLU module 210 may process the domainclassification/selection results using a meta-intent classifier 336 a.The meta-intent classifier 336 a may determine categories that describethe user's intent. An intent may be an element in a pre-defined taxonomyof semantic intentions, which may indicate a purpose of a userinteraction with the assistant system 140. The NLU module 210 a mayclassify a user input into a member of the pre-defined taxonomy. Forexample, the user input may be “Play Beethoven's 5th,” and the NLUmodule 210 a may classify the input as having the intent[IN:play_music]. In particular embodiments, intents that are common tomultiple domains may be processed by the meta-intent classifier 336 a.As an example and not by way of limitation, the meta-intent classifier336 a may be based on a machine-learning model that may take the domainclassification/selection results as input and calculate a probability ofthe input being associated with a particular predefined meta-intent. TheNLU module 210 may then use a meta slot tagger 338 a to annotate one ormore meta slots for the classification result from the meta-intentclassifier 336 a. A slot may be a named sub-string corresponding to acharacter string within the user input representing a basic semanticentity. For example, a slot for “pizza” may be [ SL: dish]. Inparticular embodiments, a set of valid or expected named slots may beconditioned on the classified intent. As an example and not by way oflimitation, for the intent [IN:play_music], a valid slot may be[SL:song_name]. In particular embodiments, the meta slot tagger 338 amay tag generic slots such as references to items (e.g., the first), thetype of slot, the value of the slot, etc. In particular embodiments, theNLU module 210 may process the domain classification/selection resultsusing an intent classifier 336 b. The intent classifier 336 b maydetermine the user's intent associated with the user input. Inparticular embodiments, there may be one intent classifier 336 b foreach domain to determine the most possible intents in a given domain. Asan example and not by way of limitation, the intent classifier 336 b maybe based on a machine-learning model that may take the domainclassification/selection results as input and calculate a probability ofthe input being associated with a particular predefined intent. The NLUmodule 210 may then use a slot tagger 338 b to annotate one or moreslots associated with the user input. In particular embodiments, theslot tagger 338 b may annotate the one or more slots for the n-grams ofthe user input. As an example and not by way of limitation, a user inputmay comprise “change 500 dollars in my account to Japanese yen.” Theintent classifier 336 b may take the user input as input and formulateit into a vector. The intent classifier 336 b may then calculateprobabilities of the user input being associated with differentpredefined intents based on a vector comparison between the vectorrepresenting the user input and the vectors representing differentpredefined intents. In a similar manner, the slot tagger 338 b may takethe user input as input and formulate each word into a vector. The slottagger 338 b may then calculate probabilities of each word beingassociated with different predefined slots based on a vector comparisonbetween the vector representing the word and the vectors representingdifferent predefined slots. The intent of the user may be classified as“changing money”. The slots of the user input may comprise “500”,“dollars”, “account”, and “Japanese yen”. The meta-intent of the usermay be classified as “financial service”. The meta slot may comprise“finance”.

In particular embodiments, the natural-language understanding (NLU)module 210 may additionally extract information from one or more of asocial graph, a knowledge graph, or a concept graph, and may retrieve auser's profile stored locally on the client system 130. The NLU module210 may additionally consider contextual information when analyzing theuser input. The NLU module 210 may further process information fromthese different sources by identifying and aggregating information,annotating n-grams of the user input, ranking the n-grams withconfidence scores based on the aggregated information, and formulatingthe ranked n-grams into features that may be used by the NLU module 210for understanding the user input. In particular embodiments, the NLUmodule 210 may identify one or more of a domain, an intent, or a slotfrom the user input in a personalized and context-aware manner. As anexample and not by way of limitation, a user input may comprise “show mehow to get to the coffee shop.” The NLU module 210 may identify aparticular coffee shop that the user wants to go to based on the user'spersonal information and the associated contextual information. Inparticular embodiments, the NLU module 210 may comprise a lexicon of aparticular language, a parser, and grammar rules to partition sentencesinto an internal representation. The NLU module 210 may also compriseone or more programs that perform naive semantics or stochastic semanticanalysis, and may further use pragmatics to understand a user input. Inparticular embodiments, the parser may be based on a deep learningarchitecture comprising multiple long-short term memory (LSTM) networks.As an example and not by way of limitation, the parser may be based on arecurrent neural network grammar (RNNG) model, which is a type ofrecurrent and recursive LSTM algorithm. More information onnatural-language understanding (NLU) may be found in U.S. patentapplication Ser. No. 16/011062, filed 18 Jun. 2018, U.S. patentapplication Ser. No. 16/025317, filed 2 Jul. 2018, and U.S. patentapplication Ser. No. 16/038120, filed 17 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the output of the NLU module 210 may be sentto the entity resolution module 212 to resolve relevant entities.Entities may include, for example, unique users or concepts, each ofwhich may have a unique identifier (ID). The entities may include one ormore of a real-world entity (from general knowledge base), a user entity(from user memory), a contextual entity (device context/dialog context),or a value resolution (numbers, datetime, etc.). In particularembodiments, the entity resolution module 212 may comprise domain entityresolution 340 and generic entity resolution 342. The entity resolutionmodule 212 may execute generic and domain-specific entity resolution.The generic entity resolution 342 may resolve the entities bycategorizing the slots and meta slots into different generic topics. Thedomain entity resolution 340 may resolve the entities by categorizingthe slots and meta slots into different domains. As an example and notby way of limitation, in response to the input of an inquiry of theadvantages of a particular brand of electric car, the generic entityresolution 342 may resolve the referenced brand of electric car asvehicle and the domain entity resolution 340 may resolve the referencedbrand of electric car as electric car.

In particular embodiments, entities may be resolved based on knowledge350 about the world and the user. The assistant system 140 may extractontology data from the graphs 352. As an example and not by way oflimitation, the graphs 352 may comprise one or more of a knowledgegraph, a social graph, or a concept graph. The ontology data maycomprise the structural relationship between different slots/meta-slotsand domains. The ontology data may also comprise information of how theslots/meta-slots may be grouped, related within a hierarchy where thehigher level comprises the domain, and subdivided according tosimilarities and differences. For example, the knowledge graph maycomprise a plurality of entities. Each entity may comprise a singlerecord associated with one or more attribute values. The particularrecord may be associated with a unique entity identifier. Each recordmay have diverse values for an attribute of the entity. Each attributevalue may be associated with a confidence probability and/or a semanticweight. A confidence probability for an attribute value represents aprobability that the value is accurate for the given attribute. Asemantic weight for an attribute value may represent how the valuesemantically appropriate for the given attribute considering all theavailable information. For example, the knowledge graph may comprise anentity of a book titled “BookName”, which may include informationextracted from multiple content sources (e.g., an online social network,online encyclopedias, book review sources, media databases, andentertainment content sources), which may be deduped, resolved, andfused to generate the single unique record for the knowledge graph. Inthis example, the entity titled “BookName” may be associated with a“fantasy” attribute value for a “genre” entity attribute. Moreinformation on the knowledge graph may be found in U.S. patentapplication Ser. No. 16/048049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048101, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the assistant user memory (AUM) 354 maycomprise user episodic memories which help determine how to assist auser more effectively. The AUM 354 may be the central place for storing,retrieving, indexing, and searching over user data. As an example andnot by way of limitation, the AUM 354 may store information such ascontacts, photos, reminders, etc. Additionally, the AUM 354 mayautomatically synchronize data to the server and other devices (only fornon-sensitive data). As an example and not by way of limitation, if theuser sets a nickname for a contact on one device, all devices maysynchronize and get that nickname based on the AUM 354. In particularembodiments, the AUM 354 may first prepare events, user sate, reminder,and trigger state for storing in a data store. Memory node identifiers(ID) may be created to store entry objects in the AUM 354, where anentry may be some piece of information about the user (e.g., photo,reminder, etc.) As an example and not by way of limitation, the firstfew bits of the memory node ID may indicate that this is a memory nodeID type, the next bits may be the user ID, and the next bits may be thetime of creation. The AUM 354 may then index these data for retrieval asneeded. Index ID may be created for such purpose. In particularembodiments, given an “index key” (e.g., PHOTO_LOCATION) and “indexvalue” (e.g., “San Francisco”), the AUM 354 may get a list of memory IDsthat have that attribute (e.g., photos in San Francisco). As an exampleand not by way of limitation, the first few bits may indicate this is anindex ID type, the next bits may be the user ID, and the next bits mayencode an “index key” and “index value”. The AUM 354 may further conductinformation retrieval with a flexible query language. Relation index IDmay be created for such purpose. In particular embodiments, given asource memory node and an edge type, the AUM 354 may get memory IDs ofall target nodes with that type of outgoing edge from the source. As anexample and not by way of limitation, the first few bits may indicatethis is a relation index ID type, the next bits may be the user ID, andthe next bits may be a source node ID and edge type. In particularembodiments, the AUM 354 may help detect concurrent updates of differentevents. More information on episodic memories may be found in U.S.patent application Ser. No. 16/552559, filed 27 Aug. 2019, which isincorporated by reference.

In particular embodiments, the entity resolution module 212 may usedifferent techniques to resolve different types of entities. Forreal-world entities, the entity resolution module 212 may use aknowledge graph to resolve the span to the entities, such as “musictrack”, “movie”, etc. For user entities, the entity resolution module212 may use user memory or some agents to resolve the span touser-specific entities, such as “contact”, “reminders”, or“relationship”. For contextual entities, the entity resolution module212 may perform coreference based on information from the context engine220 to resolve the references to entities in the context, such as “him”,“her”, “the first one”, or “the last one”. In particular embodiments,for coreference, the entity resolution module 212 may create referencesfor entities determined by the NLU module 210. The entity resolutionmodule 212 may then resolve these references accurately. As an exampleand not by way of limitation, a user input may comprise “find me thenearest grocery store and direct me there”. Based on coreference, theentity resolution module 212 may interpret “there” as “the nearestgrocery store”. In particular embodiments, coreference may depend on theinformation from the context engine 220 and the dialog manager 216 so asto interpret references with improved accuracy. In particularembodiments, the entity resolution module 212 may additionally resolvean entity under the context (device context or dialog context), such as,for example, the entity shown on the screen or an entity from the lastconversation history. For value resolutions, the entity resolutionmodule 212 may resolve the mention to exact value in standardized form,such as numerical value, date time, address, etc.

In particular embodiments, the entity resolution module 212 may firstperform a check on applicable privacy constraints in order to guaranteethat performing entity resolution does not violate any applicableprivacy policies. As an example and not by way of limitation, an entityto be resolved may be another user who specifies in their privacysettings that their identity should not be searchable on the onlinesocial network. In this case, the entity resolution module 212 mayrefrain from returning that user's entity identifier in response to auser input. By utilizing the described information obtained from thesocial graph, the knowledge graph, the concept graph, and the userprofile, and by complying with any applicable privacy policies, theentity resolution module 212 may resolve entities associated with a userinput in a personalized, context-aware, and privacy-protected manner.

In particular embodiments, the entity resolution module 212 may workwith the ASR module 208 to perform entity resolution. The followingexample illustrates how the entity resolution module 212 may resolve anentity name. The entity resolution module 212 may first expand namesassociated with a user into their respective normalized text forms asphonetic consonant representations which may be phonetically transcribedusing a double metaphone algorithm. The entity resolution module 212 maythen determine an n-best set of candidate transcriptions and perform aparallel comprehension process on all of the phonetic transcriptions inthe n-best set of candidate transcriptions. In particular embodiments,each transcription that resolves to the same intent may then becollapsed into a single intent. Each intent may then be assigned a scorecorresponding to the highest scoring candidate transcription for thatintent. During the collapse, the entity resolution module 212 mayidentify various possible text transcriptions associated with each slot,correlated by boundary timing offsets associated with the slot'stranscription. The entity resolution module 212 may then extract asubset of possible candidate transcriptions for each slot from aplurality (e.g., 1000) of candidate transcriptions, regardless ofwhether they are classified to the same intent. In this manner, theslots and intents may be scored lists of phrases. In particularembodiments, a new or running task capable of handling the intent may beidentified and provided with the intent (e.g., a message compositiontask for an intent to send a message to another user). The identifiedtask may then trigger the entity resolution module 212 by providing itwith the scored lists of phrases associated with one of its slots andthe categories against which it should be resolved. As an example andnot by way of limitation, if an entity attribute is specified as“friend,” the entity resolution module 212 may run every candidate listof terms through the same expansion that may be run at matchercompilation time. Each candidate expansion of the terms may be matchedin the precompiled trie matching structure. Matches may be scored usinga function based at least in part on the transcribed input, matchedform, and friend name. As another example and not by way of limitation,if an entity attribute is specified as “celebrity/notable person,” theentity resolution module 212 may perform parallel searches against theknowledge graph for each candidate set of terms for the slot output fromthe ASR module 208. The entity resolution module 212 may score matchesbased on matched person popularity and ASR-provided score signal. Inparticular embodiments, when the memory category is specified, theentity resolution module 212 may perform the same search against usermemory. The entity resolution module 212 may crawl backward through usermemory and attempt to match each memory (e.g., person recently mentionedin conversation, or seen and recognized via visual signals, etc.). Foreach entity, the entity resolution module 212 may employ matchingsimilarly to how friends are matched (i.e., phonetic). In particularembodiments, scoring may comprise a temporal decay factor associatedwith a recency with which the name was previously mentioned. The entityresolution module 212 may further combine, sort, and dedupe all matches.In particular embodiments, the task may receive the set of candidates.When multiple high scoring candidates are present, the entity resolutionmodule 212 may perform user-facilitated disambiguation (e.g., gettingreal-time user feedback from users on these candidates).

In particular embodiments, the context engine 220 may help the entityresolution module 212 improve entity resolution. The context engine 220may comprise offline aggregators and an online inference service. Theoffline aggregators may process a plurality of data associated with theuser that are collected from a prior time window. As an example and notby way of limitation, the data may include news feed posts/comments,interactions with news feed posts/comments, search history, etc., thatare collected during a predetermined timeframe (e.g., from a prior90-day window). The processing result may be stored in the contextengine 220 as part of the user profile. The user profile of the user maycomprise user profile data including demographic information, socialinformation, and contextual information associated with the user. Theuser profile data may also include user interests and preferences on aplurality of topics, aggregated through conversations on news feed,search logs, messaging platforms, etc. The usage of a user profile maybe subject to privacy constraints to ensure that a user's informationcan be used only for his/her benefit, and not shared with anyone else.More information on user profiles may be found in U.S. patentapplication Ser. No. 15/967239, filed 30 Apr. 2018, which isincorporated by reference. In particular embodiments, the onlineinference service may analyze the conversational data associated withthe user that are received by the assistant system 140 at a currenttime. The analysis result may be stored in the context engine 220 alsoas part of the user profile. In particular embodiments, both the offlineaggregators and online inference service may extract personalizationfeatures from the plurality of data. The extracted personalizationfeatures may be used by other modules of the assistant system 140 tobetter understand user input. In particular embodiments, the entityresolution module 212 may process the information from the contextengine 220 (e.g., a user profile) in the following steps based onnatural-language processing (NLP). In particular embodiments, the entityresolution module 212 may tokenize text by text normalization, extractsyntax features from text, and extract semantic features from text basedon NLP. The entity resolution module 212 may additionally extractfeatures from contextual information, which is accessed from dialoghistory between a user and the assistant system 140. The entityresolution module 212 may further conduct global word embedding,domain-specific embedding, and/or dynamic embedding based on thecontextual information. The processing result may be annotated withentities by an entity tagger. Based on the annotations, the entityresolution module 212 may generate dictionaries. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. The entity resolution module212 may rank the entities tagged by the entity tagger. In particularembodiments, the entity resolution module 212 may communicate withdifferent graphs 352 including one or more of the social graph, theknowledge graph, or the concept graph to extract ontology data that isrelevant to the retrieved information from the context engine 220. Inparticular embodiments, the entity resolution module 212 may furtherresolve entities based on the user profile, the ranked entities, and theinformation from the graphs 352.

In particular embodiments, the entity resolution module 212 may bedriven by the task (corresponding to an agent 228). This inversion ofprocessing order may make it possible for domain knowledge present in atask to be applied to pre-filter or bias the set of resolution targetswhen it is obvious and appropriate to do so. As an example and not byway of limitation, for the utterance “who is John?” no clear category isimplied in the utterance. Therefore, the entity resolution module 212may resolve “John” against everything. As another example and not by wayof limitation, for the utterance “send a message to John”, the entityresolution module 212 may easily determine “John” refers to a personthat one can message. As a result, the entity resolution module 212 maybias the resolution to a friend. As another example and not by way oflimitation, for the utterance “what is John's most famous album?” Toresolve “John”, the entity resolution module 212 may first determine thetask corresponding to the utterance, which is finding a music album. Theentity resolution module 212 may determine that entities related tomusic albums include singers, producers, and recording studios.Therefore, the entity resolution module 212 may search among these typesof entities in a music domain to resolve “John.”

In particular embodiments, the output of the entity resolution module212 may be sent to the dialog manager 216 to advance the flow of theconversation with the user. The dialog manager 216 may be anasynchronous state machine that repeatedly updates the state and selectsactions based on the new state. The dialog manager 216 may additionallystore previous conversations between the user and the assistant system140. In particular embodiments, the dialog manager 216 may conductdialog optimization. Dialog optimization relates to the challenge ofunderstanding and identifying the most likely branching options in adialog with a user. As an example and not by way of limitation, theassistant system 140 may implement dialog optimization techniques toobviate the need to confirm who a user wants to call because theassistant system 140 may determine a high confidence that a personinferred based on context and available data is the intended recipient.In particular embodiments, the dialog manager 216 may implementreinforcement learning frameworks to improve the dialog optimization.The dialog manager 216 may comprise dialog intent resolution 356, thedialog state tracker 218, and the action selector 222. In particularembodiments, the dialog manager 216 may execute the selected actions andthen call the dialog state tracker 218 again until the action selectedrequires a user response, or there are no more actions to execute. Eachaction selected may depend on the execution result from previousactions. In particular embodiments, the dialog intent resolution 356 mayresolve the user intent associated with the current dialog session basedon dialog history between the user and the assistant system 140. Thedialog intent resolution 356 may map intents determined by the NLUmodule 210 to different dialog intents. The dialog intent resolution 356may further rank dialog intents based on signals from the NLU module210, the entity resolution module 212, and dialog history between theuser and the assistant system 140.

In particular embodiments, the dialog state tracker 218 may use a set ofoperators to track the dialog state. The operators may comprisenecessary data and logic to update the dialog state. Each operator mayact as delta of the dialog state after processing an incoming userinput. In particular embodiments, the dialog state tracker 218 may acomprise a task tracker, which may be based on task specifications anddifferent rules. The dialog state tracker 218 may also comprise a slottracker and coreference component, which may be rule based and/orrecency based. The coreference component may help the entity resolutionmodule 212 to resolve entities. In alternative embodiments, with thecoreference component, the dialog state tracker 218 may replace theentity resolution module 212 and may resolve any references/mentions andkeep track of the state.In particular embodiments, the dialog statetracker 218 may convert the upstream results into candidate tasks usingtask specifications and resolve arguments with entity resolution. Bothuser state (e.g., user's current activity) and task state (e.g.,triggering conditions) may be tracked. Given the current state, thedialog state tracker 218 may generate candidate tasks the assistantsystem 140 may process and perform for the user. As an example and notby way of limitation, candidate tasks may include “show suggestion,”“get weather information,” or “take photo.” In particular embodiments,the dialog state tracker 218 may generate candidate tasks based onavailable data from, for example, a knowledge graph, a user memory, anda user task history. In particular embodiments, the dialog state tracker218 may then resolve the triggers object using the resolved arguments.As an example and not by way of limitation, a user input “remind me tocall mom when she's online and I'm home tonight” may perform theconversion from the NLU output to the triggers representation by thedialog state tracker 218 as illustrated in Table 1 below:

TABLE 1 Example Conversion from NLU Output to Triggers RepresentationNLU Ontology Representation: Triggers Representation:[IN:CREATE_SMART_REMINDER →  Triggers: { Remind me to   andTriggers: [ [SL:TODO call mom] when  [SL:TRIGGER_CONJUNCTION   condition:{ContextualEvent(mom is   [IN:GET_TRIGGER   online)},   [SL:TRIGGER_SOCIAL_UPDATE   condition: {ContextualEvent(location is   she's online] and I'm   home)},    [SL:TRIGGER_LOCATION home]  condition: {ContextualEvent(time is    [SL:DATE_TIME tonight]  tonight)}]))]}   ]  ] ]In the above example, “mom,” “home,” and “tonight” are represented bytheir respective entities: personEntity, locationEntity, datetimeEntity.

In particular embodiments, the dialog manager 216 may map eventsdetermined by the context engine 220 to actions. As an example and notby way of limitation, an action may be a natural-language generation(NLG) action, a display or overlay, a device action, or a retrievalaction. The dialog manager 216 may also perform context tracking andinteraction management. Context tracking may comprise aggregatingreal-time stream of events into a unified user state. Interactionmanagement may comprise selecting optimal action in each state. Inparticular embodiments, the dialog state tracker 218 may perform contexttracking (i.e., tracking events related to the user). To supportprocessing of event streams, the dialog state tracker 218 a may use anevent handler (e.g., for disambiguation, confirmation, request) that mayconsume various types of events and update an internal assistant state.Each event type may have one or more handlers. Each event handler may bemodifying a certain slice of the assistant state. In particularembodiments, the event handlers may be operating on disjoint subsets ofthe state (i.e., only one handler may have write-access to a particularfield in the state). In particular embodiments, all event handlers mayhave an opportunity to process a given event. As an example and not byway of limitation, the dialog state tracker 218 may run all eventhandlers in parallel on every event, and then may merge the stateupdates proposed by each event handler (e.g., for each event, mosthandlers may return a NULL update).

In particular embodiments, the dialog state tracker 218 may work as anyprogrammatic handler (logic) that requires versioning. In particularembodiments, instead of directly altering the dialog state, the dialogstate tracker 218 may be a side-effect free component and generaten-best candidates of dialog state update operators that propose updatesto the dialog state. The dialog state tracker 218 may comprise intentresolvers containing logic to handle different types of NLU intent basedon the dialog state and generate the operators. In particularembodiments, the logic may be organized by intent handler, such as adisambiguation intent handler to handle the intents when the assistantsystem 140 asks for disambiguation, a confirmation intent handler thatcomprises the logic to handle confirmations, etc. Intent resolvers maycombine the turn intent together with the dialog state to generate thecontextual updates for a conversation with the user. A slot resolutioncomponent may then recursively resolve the slots in the update operatorswith resolution providers including the knowledge graph and domainagents. In particular embodiments, the dialog state tracker 218 mayupdate/rank the dialog state of the current dialog session. As anexample and not by way of limitation, the dialog state tracker 218 mayupdate the dialog state as “completed” if the dialog session is over. Asanother example and not by way of limitation, the dialog state tracker218 may rank the dialog state based on a priority associated with it.

In particular embodiments, the dialog state tracker 218 may communicatewith the action selector 222 about the dialog intents and associatedcontent objects. In particular embodiments, the action selector 222 mayrank different dialog hypotheses for different dialog intents. Theaction selector 222 may take candidate operators of dialog state andconsult the dialog policies 360 to decide what actions should beexecuted. In particular embodiments, a dialog policy 360 may atree-based policy, which is a pre-constructed dialog plan. Based on thecurrent dialog state, a dialog policy 360 may choose a node to executeand generate the corresponding actions. As an example and not by way oflimitation, the tree-based policy may comprise topic grouping nodes anddialog action (leaf) nodes. In particular embodiments, a dialog policy360 may also comprise a data structure that describes an execution planof an action by an agent 228. A dialog policy 360 may further comprisemultiple goals related to each other through logical operators. Inparticular embodiments, a goal may be an outcome of a portion of thedialog policy and it may be constructed by the dialog manager 216. Agoal may be represented by an identifier (e.g., string) with one or morenamed arguments, which parameterize the goal. As an example and not byway of limitation, a goal with its associated goal argument may berepresented as {confirm_artist, args: {artist: “Madonna”}}. Inparticular embodiments, goals may be mapped to leaves of the tree of thetree-structured representation of the dialog policy 360.

In particular embodiments, the assistant system 140 may use hierarchicaldialog policies 360 with general policy 362 handling the cross-domainbusiness logic and task policies 364 handling the task/domain specificlogic. The general policy 362 may be used for actions that are notspecific to individual tasks. The general policy 362 may be used todetermine task stacking and switching, proactive tasks, notifications,etc. The general policy 362 may comprise handling low-confidenceintents, internal errors, unacceptable user response with retries,and/or skipping or inserting confirmation based on ASR or NLU confidencescores. The general policy 362 may also comprise the logic of rankingdialog state update candidates from the dialog state tracker 218 outputand pick the one to update (such as picking the top ranked task intent).In particular embodiments, the assistant system 140 may have aparticular interface for the general policy 362, which allows forconsolidating scattered cross-domain policy/business-rules, especialthose found in the dialog state tracker 218, into a function of theaction selector 222. The interface for the general policy 362 may alsoallow for authoring of self-contained sub-policy units that may be tiedto specific situations or clients (e.g., policy functions that may beeasily switched on or off based on clients, situation). The interfacefor the general policy 362 may also allow for providing a layering ofpolicies with back-off, i.e., multiple policy units, with highlyspecialized policy units that deal with specific situations being backedup by more general policies 362 that apply in wider circumstances. Inthis context the general policy 362 may alternatively comprise intent ortask specific policy.

In particular embodiments, a task policy 364 may comprise the logic foraction selector 222 based on the task and current state. The task policy364 may be dynamic and ad-hoc. In particular embodiments, the types oftask policies 364 may include one or more of the following types: (1)manually crafted tree-based dialog plans; (2) coded policy that directlyimplements the interface for generating actions; (3)configurator-specified slot-filling tasks; or (4) machine-learning modelbased policy learned from data. In particular embodiments, the assistantsystem 140 may bootstrap new domains with rule-based logic and laterrefine the task policies 364 with machine-learning models. In particularembodiments, the general policy 362 may pick one operator from thecandidate operators to update the dialog state, followed by theselection of a user facing action by a task policy 364. Once a task isactive in the dialog state, the corresponding task policy 364 may beconsulted to select right actions.

In particular embodiments, the action selector 222 may select an actionbased on one or more of the event determined by the context engine 220,the dialog intent and state, the associated content objects, and theguidance from dialog policies 360. Each dialog policy 360 may besubscribed to specific conditions over the fields of the state. After anevent is processed and the state is updated, the action selector 222 mayrun a fast search algorithm (e.g., similarly to the Booleansatisfiability) to identify which policies should be triggered based onthe current state. In particular embodiments, if multiple policies aretriggered, the action selector 222 may use a tie-breaking mechanism topick a particular policy. Alternatively, the action selector 222 may usea more sophisticated approach which may dry-run each policy and thenpick a particular policy which may be determined to have a highlikelihood of success. In particular embodiments, mapping events toactions may result in several technical advantages for the assistantsystem 140. One technical advantage may include that each event may be astate update from the user or the user's physical/digital environment,which may or may not trigger an action from assistant system 140.Another technical advantage may include possibilities to handle rapidbursts of events (e.g., user enters a new building and sees many people)by first consuming all events to update state, and then triggeringaction(s) from the final state. Another technical advantage may includeconsuming all events into a single global assistant state.

In particular embodiments, the action selector 222 may take the dialogstate update operators as part of the input to select the dialog action.The execution of the dialog action may generate a set of expectations toinstruct the dialog state tracker 218 to handle future turns. Inparticular embodiments, an expectation may be used to provide context tothe dialog state tracker 218 when handling the user input from nextturn. As an example and not by way of limitation, slot request dialogaction may have the expectation of proving a value for the requestedslot. In particular embodiments, both the dialog state tracker 218 andthe action selector 222 may not change the dialog state until theselected action is executed. This may allow the assistant system 140 toexecute the dialog state tracker 218 and the action selector 222 forprocessing speculative ASR results and to do n-best ranking with dryruns.

In particular embodiments, the action selector 222 may call differentagents 228 for task execution. Meanwhile, the dialog manager 216 mayreceive an instruction to update the dialog state. As an example and notby way of limitation, the update may comprise awaiting agents' 228response. An agent 228 may select among registered content providers tocomplete the action. The data structure may be constructed by the dialogmanager 216 based on an intent and one or more slots associated with theintent. In particular embodiments, the agents 228 may comprisefirst-party agents and third-party agents. In particular embodiments,first-party agents may comprise internal agents that are accessible andcontrollable by the assistant system 140 (e.g. agents associated withservices provided by the online social network, such as messagingservices or photo-share services). In particular embodiments,third-party agents may comprise external agents that the assistantsystem 140 has no control over (e.g., third-party online musicapplication agents, ticket sales agents). The first-party agents may beassociated with first-party providers that provide content objectsand/or services hosted by the social-networking system 160. Thethird-party agents may be associated with third-party providers thatprovide content objects and/or services hosted by the third-party system170. In particular embodiments, each of the first-party agents orthird-party agents may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, shopping, social, videos, photos, events,locations, and/or work. In particular embodiments, the assistant system140 may use a plurality of agents 228 collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, the dialog manager 216 may support multi-turncompositional resolution of slot mentions. For a compositional parsefrom the NLU module 210, the resolver may recursively resolve the nestedslots. The dialog manager 216 may additionally support disambiguationfor the nested slots. As an example and not by way of limitation, theuser input may be “remind me to call Alex”. The resolver may need toknow which Alex to call before creating an actionable reminder to-doentity. The resolver may halt the resolution and set the resolutionstate when further user clarification is necessary for a particularslot. The general policy 362 may examine the resolution state and createcorresponding dialog action for user clarification. In dialog statetracker 218, based on the user input and the last dialog action, thedialog manager 216 may update the nested slot. This capability may allowthe assistant system 140 to interact with the user not only to collectmissing slot values but also to reduce ambiguity of morecomplex/ambiguous utterances to complete the task. In particularembodiments, the dialog manager 216 may further support requestingmissing slots in a nested intent and multi-intent user inputs (e.g.,“take this photo and send it to Dad”). In particular embodiments, thedialog manager 216 may support machine-learning models for more robustdialog experience. As an example and not by way of limitation, thedialog state tracker 218 may use neural network based models (or anyother suitable machine-learning models) to model belief over taskhypotheses. As another example and not by way of limitation, for actionselector 222, highest priority policy units may comprisewhite-list/black-list overrides, which may have to occur by design;middle priority units may comprise machine-learning models designed foraction selection; and lower priority units may comprise rule-basedfallbacks when the machine-learning models elect not to handle asituation. In particular embodiments, machine-learning model basedgeneral policy unit may help the assistant system 140 reduce redundantdisambiguation or confirmation steps, thereby reducing the number ofturns to execute the user input.

In particular embodiments, the determined actions by the action selector222 may be sent to the delivery system 230. The delivery system 230 maycomprise a CU composer 370, a response generation component 380, adialog state writing component 382, and a text-to-speech (TTS) component390. Specifically, the output of the action selector 222 may be receivedat the CU composer 370. In particular embodiments, the output from theaction selector 222 may be formulated as a <k,c,u,d> tuple, in which kindicates a knowledge source, c indicates a communicative goal, uindicates a user model, and d indicates a discourse model.

In particular embodiments, the CU composer 370 may generate acommunication content for the user using a natural-language generation(NLG) component 372. In particular embodiments, the NLG component 372may use different language models and/or language templates to generatenatural-language outputs. The generation of natural-language outputs maybe application specific. The generation of natural-language outputs maybe also personalized for each user. In particular embodiments, the NLGcomponent 372 may comprise a content determination component, a sentenceplanner, and a surface realization component. The content determinationcomponent may determine the communication content based on the knowledgesource, communicative goal, and the user's expectations. As an exampleand not by way of limitation, the determining may be based on adescription logic. The description logic may comprise, for example,three fundamental notions which are individuals (representing objects inthe domain), concepts (describing sets of individuals), and roles(representing binary relations between individuals or concepts). Thedescription logic may be characterized by a set of constructors thatallow the natural-language generator to build complex concepts/rolesfrom atomic ones. In particular embodiments, the content determinationcomponent may perform the following tasks to determine the communicationcontent. The first task may comprise a translation task, in which theinput to the NLG component 372 may be translated to concepts. The secondtask may comprise a selection task, in which relevant concepts may beselected among those resulted from the translation task based on theuser model. The third task may comprise a verification task, in whichthe coherence of the selected concepts may be verified. The fourth taskmay comprise an instantiation task, in which the verified concepts maybe instantiated as an executable file that can be processed by the NLGcomponent 372. The sentence planner may determine the organization ofthe communication content to make it human understandable. The surfacerealization component may determine specific words to use, the sequenceof the sentences, and the style of the communication content.

In particular embodiments, the CU composer 370 may also determine amodality of the generated communication content using the UI payloadgenerator 374. Since the generated communication content may beconsidered as a response to the user input, the CU composer 370 mayadditionally rank the generated communication content using a responseranker 376. As an example and not by way of limitation, the ranking mayindicate the priority of the response. In particular embodiments, the CUcomposer 370 may comprise a natural-language synthesis (NLS) componentthat may be separate from the NLG component 372. The NLS component mayspecify attributes of the synthesized speech generated by the CUcomposer 370, including gender, volume, pace, style, or register, inorder to customize the response for a particular user, task, or agent.The NLS component may tune language synthesis without engaging theimplementation of associated tasks. In particular embodiments, the CUcomposer 370 may check privacy constraints associated with the user tomake sure the generation of the communication content follows theprivacy policies. More information on customizing natural-languagegeneration (NLG) may be found in U.S. patent application Ser. No.15/967279, filed 30 Apr. 2018, and U.S. patent application Ser. No.15/966455, filed 30 Apr. 2018, which is incorporated by reference.

In particular embodiments, the delivery system 230 may perform differenttasks based on the output of the CU composer 370. These tasks mayinclude writing (i.e., storing/updating) the dialog state into the datastore 330 using the dialog state writing component 382 and generatingresponses using the response generation component 380. In particularembodiments, the output of the CU composer 370 may be additionally sentto the TTS component 390 if the determined modality of the communicationcontent is audio. In particular embodiments, the output from thedelivery system 230 comprising one or more of the generated responses,the communication content, or the speech generated by the TTS component390 may be then sent back to the dialog manager 216.

In particular embodiments, the orchestrator 206 may determine, based onthe output of the entity resolution module 212, whether to processing auser input on the client system 130 or on the server, or in the thirdoperational mode (i.e., blended mode) using both. Besides determininghow to process the user input, the orchestrator 206 may receive theresults from the agents 228 and/or the results from the delivery system230 provided by the dialog manager 216. The orchestrator 206 may thenforward these results to the arbitrator 226. The arbitrator 226 mayaggregate these results, analyze them, select the best result, andprovide the selected result to the render output module 232. Inparticular embodiments, the arbitrator 226 may consult with dialogpolicies 360 to obtain the guidance when analyzing these results. Inparticular embodiments, the render output module 232 may generate aresponse that is suitable for the client system 130.

FIG. 4 illustrates an example task-centric flow diagram 400 ofprocessing a user input. In particular embodiments, the assistant system140 may assist users not only with voice-initiated experiences but alsomore proactive, multi-modal experiences that are initiated onunderstanding user context. In particular embodiments, the assistantsystem 140 may rely on assistant tasks for such purpose. An assistanttask may be a central concept that is shared across the whole assistantstack to understand user intention, interact with the user and the worldto complete the right task for the user. In particular embodiments, anassistant task may be the primitive unit of assistant capability. It maycomprise data fetching, updating some state, executing some command, orcomplex tasks composed of a smaller set of tasks. Completing a taskcorrectly and successfully to deliver the value to the user may be thegoal that the assistant system 140 is optimized for. In particularembodiments, an assistant task may be defined as a capability or afeature. The assistant task may be shared across multiple productsurfaces if they have exactly the same requirements so it may be easilytracked. It may also be passed from device to device, and easily pickedup mid-task by another device since the primitive unit is consistent. Inaddition, the consistent format of the assistant task may allowdevelopers working on different modules in the assistant stack to moreeasily design around it. Furthermore, it may allow for task sharing. Asan example and not by way of limitation, if a user is listening to musicon smart glasses, the user may say “play this music on my phone.” In theevent that the phone hasn't been woken or has a task to execute, thesmart glasses may formulate a task that is provided to the phone, whichmay then be executed by the phone to start playing music. In particularembodiments, the assistant task may be retained by each surfaceseparately if they have different expected behaviors. In particularembodiments, the assistant system 140 may identify the right task basedon user inputs in different modality or other signals, conductconversation to collect all necessary information, and complete thattask with action selector 222 implemented internally or externally, onserver or locally product surfaces. In particular embodiments, theassistant stack may comprise a set of processing components fromwake-up, recognizing user inputs, understanding user intention,reasoning about the tasks, fulfilling a task to generatenatural-language response with voices.

In particular embodiments, the user input may comprise speech input. Thespeech input may be received at the ASR module 208 for extracting thetext transcription from the speech input. The ASR module 208 may usestatistical models to determine the most likely sequences of words thatcorrespond to a given portion of speech received by the assistant system140 as audio input. The models may include one or more of hidden Markovmodels, neural networks, deep learning models, or any combinationthereof. The received audio input may be encoded into digital data at aparticular sampling rate (e.g., 16, 44.1, or 96 kHz) and with aparticular number of bits representing each sample (e.g., 8, 16, of 24bits).

In particular embodiments, the ASR module 208 may comprise one or moreof a grapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized acoustic model, a personalized language model (PLM), or anend-pointing model. In particular embodiments, the grapheme-to-phoneme(G2P) model may be used to determine a user's grapheme-to-phoneme style(i.e., what it may sound like when a particular user speaks a particularword). In particular embodiments, the personalized acoustic model may bea model of the relationship between audio signals and the sounds ofphonetic units in the language. Therefore, such personalized acousticmodel may identify how a user's voice sounds. The personalizedacoustical model may be generated using training data such as trainingspeech received as audio input and the corresponding phonetic units thatcorrespond to the speech. The personalized acoustical model may betrained or refined using the voice of a particular user to recognizethat user's speech. In particular embodiments, the personalized languagemodel may then determine the most likely phrase that corresponds to theidentified phonetic units for a particular audio input. The personalizedlanguage model may be a model of the probabilities that various wordsequences may occur in the language. The sounds of the phonetic units inthe audio input may be matched with word sequences using thepersonalized language model, and greater weights may be assigned to theword sequences that are more likely to be phrases in the language. Theword sequence having the highest weight may be then selected as the textthat corresponds to the audio input. In particular embodiments, thepersonalized language model may also be used to predict what words auser is most likely to say given a context. In particular embodiments,the end-pointing model may detect when the end of an utterance isreached. In particular embodiments, based at least in part on a limitedcomputing power of the client system 130, the assistant system 140 mayoptimize the personalized language model at runtime during theclient-side process. As an example and not by way of limitation, theassistant system 140 may pre-compute a plurality of personalizedlanguage models for a plurality of possible subjects a user may talkabout. When a user input is associated with a request for assistance,the assistant system 140 may promptly switch between and locallyoptimize the pre-computed language models at runtime based on useractivities. As a result, the assistant system 140 may preservecomputational resources while efficiently identifying a subject matterassociated with the user input. In particular embodiments, the assistantsystem 140 may also dynamically re-learn user pronunciations at runtime.

In particular embodiments, the user input may comprise non-speech input.The non-speech input may be received at the context engine 220 fordetermining events and context from the non-speech input. The contextengine 220 may determine multi-modal events comprising voice/textintents, location updates, visual events, touch, gaze, gestures,activities, device/application events, and/or any other suitable type ofevents. The voice/text intents may depend on the ASR module 208 and theNLU module 210. The location updates may be consumed by the dialogmanager 216 to support various proactive/reactive scenarios. The visualevents may be based on person or object appearing in the user's field ofview. These events may be consumed by the dialog manager 216 andrecorded in transient user state to support visual co-reference (e.g.,resolving “that” in “how much is that shirt?” and resolving “him” in“send him my contact”). The gaze, gesture, and activity may result inflags being set in the transient user state (e.g., user is running)which may condition the action selector 222. For the device/applicationevents, if an application makes an update to the device state, this maybe published to the assistant system 140 so that the dialog manager 216may use this context (what is currently displayed to the user) to handlereactive and proactive scenarios. As an example and not by way oflimitation, the context engine 220 may cause a push notification messageto be displayed on a display screen of the user's client system 130. Theuser may interact with the push notification message, which may initiatea multi-modal event (e.g., an event workflow for replying to a messagereceived from another user). Other example multi-modal events mayinclude seeing a friend, seeing a landmark, being at home, running,starting a call with touch, taking a photo with touch, opening anapplication, etc. In particular embodiments, the context engine 220 mayalso determine world/social events based on world/social updates (e.g.,weather changes, a friend getting online). The social updates maycomprise events that a user is subscribed to, (e.g., friend's birthday,posts, comments, other notifications). These updates may be consumed bythe dialog manager 216 to trigger proactive actions based on context(e.g., suggesting a user call a friend on their birthday, but only ifthe user is not focused on something else). As an example and not by wayof limitation, receiving a message may be a social event, which maytrigger the task of reading the message to the user.

In particular embodiments, the text transcription from the ASR module208 may be sent to the NLU module 210. The NLU module 210 may processthe text transcription and extract the user intention (i.e., intents)and parse the slots or parsing result based on the linguistic ontology.In particular embodiments, the intents and slots from the NLU module 210and/or the events and contexts from the context engine 220 may be sentto the entity resolution module 212. In particular embodiments, theentity resolution module 212 may resolve entities associated with theuser input based on the output from the NLU module 210 and/or thecontext engine 220. The entity resolution module 212 may use differenttechniques to resolve the entities, including accessing user memory fromthe assistant user memory (AUM) 354. In particular embodiments, the AUM354 may comprise user episodic memories helpful for resolving theentities by the entity resolution module 212. The AUM 354 may be thecentral place for storing, retrieving, indexing, and searching over userdata.

In particular embodiments, the entity resolution module 212 may provideone or more of the intents, slots, entities, events, context, or usermemory to the dialog state tracker 218. The dialog state tracker 218 mayidentify a set of state candidates for a task accordingly, conductinteraction with the user to collect necessary information to fill thestate, and call the action selector 222 to fulfill the task. Inparticular embodiments, the dialog state tracker 218 may comprise a tasktracker 410. The task tracker 410 may track the task state associatedwith an assistant task. In particular embodiments, a task state may be adata structure persistent cross interaction turns and updates in realtime to capture the state of the task during the whole interaction. Thetask state may comprise all the current information about a taskexecution status, such as arguments, confirmation status, confidencescore, etc. Any incorrect or outdated information in the task state maylead to failure or incorrect task execution. The task state may alsoserve as a set of contextual information for many other components suchas the ASR module 208, the NLU module 210, etc.

In particular embodiments, the task tracker 410 may comprise intenthandlers 411, task candidate ranking module 414, task candidategeneration module 416, and merging layer 419. In particular embodiments,a task may be identified by its ID name. The task ID may be used toassociate corresponding component assets if it is not explicitly set inthe task specification, such as dialog policy 360, agent execution, NLGdialog act, etc. Therefore, the output from the entity resolution module212 may be received by a task ID resolution component 417 of the taskcandidate generation module 416 to resolve the task ID of thecorresponding task. In particular embodiments, the task ID resolutioncomponent 417 may call a task specification manager API 430 to accessthe triggering specifications and deployment specifications forresolving the task ID. Given these specifications, the task IDresolution component 417 may resolve the task ID using intents, slots,dialog state, context, and user memory.

In particular embodiments, the technical specification of a task may bedefined by a task specification. The task specification may be used bythe assistant system 140 to trigger a task, conduct dialog conversation,and find a right execution module (e.g., agents 228) to execute thetask. The task specification may be an implementation of the productrequirement document. It may serve as the general contract andrequirements that all the components agreed on. It may be considered asan assembly specification for a product, while all development partnersdeliver the modules based on the specification. In particularembodiments, an assistant task may be defined in the implementation by aspecification. As an example and not by way of limitation, the taskspecification may be defined as the following categories. One categorymay be a basic task schema which comprises the basic identificationinformation such as ID, name, and the schema of the input arguments.Another category may be a triggering specification, which is about how atask can be triggered, such as intents, event message ID, etc. Anothercategory may be a conversational specification, which is for dialogmanager 216 to conduct the conversation with users and systems. Anothercategory may be an execution specification, which is about how the taskwill be executed and fulfilled. Another category may be a deploymentspecification, which is about how a feature will be deployed to certainsurfaces, local, and group of users.

In particular embodiments, the task specification manager API 430 may bean API for accessing a task specification manager. The taskspecification manager may be a module in the runtime stack for loadingthe specifications from all the tasks and providing interfaces to accessall the tasks specifications for detailed information or generating taskcandidates. In particular embodiments, the task specification managermay be accessible for all components in the runtime stack via the taskspecification manager API 430. The task specification manager maycomprise a set of static utility functions to manage tasks with the taskspecification manager, such as filtering task candidates by platform.Before landing the task specification, the assistant system 140 may alsodynamically load the task specifications to support end-to-enddevelopment on the development stage.

In particular embodiments, the task specifications may be grouped bydomains and stored in runtime configurations 435. The runtime stack mayload all the task specifications from the runtime configurations 435during the building time. In particular embodiments, in the runtimeconfigurations 435, for a domain, there may be a cconf file and a cincfile (e.g., sidechef task.cconf and sidechef task.inc). As an exampleand not by way of limitation, <domain>_tasks.cconf may comprise all thedetails of the task specifications. As another example and not by way oflimitation, <domain>_tasks.cinc may provide a way to override thegenerated specification if there is no support for that feature yet.

In particular embodiments, a task execution may require a set ofarguments to execute. Therefore, an argument resolution component 418may resolve the argument names using the argument specifications for theresolved task ID. These arguments may be resolved based on NLU outputs(e.g., slot [SL:contact]), dialog state (e.g., short-term callinghistory), user memory (such as user preferences, location, long-termcalling history, etc.), or device context (such as timer states, screencontent, etc.). In particular embodiments, the argument modality may betext, audio, images or other structured data. The slot to argumentmapping may be defined by a filling strategy and/or language ontology.In particular embodiments, given the task triggering specifications, thetask candidate generation module 416 may look for the list of tasks tobe triggered as task candidates based on the resolved task ID andarguments.

In particular embodiments, the generated task candidates may be sent tothe task candidate ranking module 414 to be further ranked. The taskcandidate ranking module 414 may use a rule-based ranker 415 to rankthem. In particular embodiments, the rule-based ranker 415 may comprisea set of heuristics to bias certain domain tasks. The ranking logic maybe described as below with principles of context priority. In particularembodiments, the priority of a user specified task may be higher than anon-foreground task. The priority of the on-foreground task may be higherthan a device-domain task when the intent is a meta intent. The priorityof the device-domain task may be higher than a task of a triggeringintent domain. As an example and not by way of limitation, the rankingmay pick the task if the task domain is mentioned or specified in theutterance, such as “create a timer in TIMER app”. As another example andnot by way of imitation, the ranking may pick the task if the taskdomain is on foreground or active state, such as “stop the timer” tostop the timer while the TIMER app is on foreground and there is anactive timer. As yet another example and not by way of imitation, theranking may pick the task if the intent is general meta intent, and thetask is device control while there is no other active application oractive state. As yet another example and not by way of imitation, theranking may pick the task if the task is the same as the intent domain.In particular embodiments, the task candidate ranking module 414 maycustomize some more logic to check the match of intent/slot/entitytypes. The ranked task candidates may be sent to the merging layer 419.

In particular embodiments, the output from the entity resolution module212 may also sent to a task ID resolution component 412 of the intenthandlers 411. The task ID resolution component 412 may resolve the taskID of the corresponding task similarly to the task ID resolutioncomponent 417. In particular embodiments, the intent handlers 411 mayadditionally comprise an argument resolution component 413. The argumentresolution component 413 may resolve the argument names using theargument specifications for the resolved task ID similarly to theargument resolution component 418. In particular embodiments, intenthandlers 411 may deal with task agnostic features and may not beexpressed within the task specifications which are task specific. Intenthandlers 411 may output state candidates other than task candidates suchas argument update, confirmation update, disambiguation update, etc. Inparticular embodiments, some tasks may require very complex triggeringconditions or very complex argument filling logic that may not bereusable by other tasks even if they were supported in the taskspecifications (e.g., in-call voice commands, media tasks via[IN:PLAY_MEDIA], etc.). Intent handlers 411 may be also suitable forsuch type of tasks. In particular embodiments, the results from theintent handlers 411 may take precedence over the results from the taskcandidate ranking module 414. The results from the intent handlers 411may be also sent to the merging layer 419.

In particular embodiments, the merging layer 419 may combine the resultsfrom the intent handlers 411 and the results from the task candidateranking module 414. The dialog state tracker 218 may suggest each taskas a new state for the dialog policies 360 to select from, therebygenerating a list of state candidates. The merged results may be furthersent to a conversational understanding reinforcement engine (CURE)tracker 420. In particular embodiments, the CURE tracker 420 may be apersonalized learning process to improve the determination of the statecandidates by the dialog state tracker 218 under different contextsusing real-time user feedback. More information on conversationalunderstanding reinforcement engine may be found in U.S. patentapplication Ser. No. 17/186459, filed 26 Feb. 2021, which isincorporated by reference.

In particular embodiments, the state candidates generated by the CUREtracker 420 may be sent to the action selector 222. The action selector222 may consult with the task policies 364, which may be generated fromexecution specifications accessed via the task specification manager API430. In particular embodiments, the execution specifications maydescribe how a task should be executed and what actions the actionselector 222 may need to take to complete the task.

In particular embodiments, the action selector 222 may determine actionsassociated with the system. Such actions may involve the agents 228 toexecute. As a result, the action selector 222 may send the systemactions to the agents 228 and the agents 228 may return the executionresults of these actions. In particular embodiments, the action selectormay determine actions associated with the user or device. Such actionsmay need to be executed by the delivery system 230. As a result, theaction selector 222 may send the user/device actions to the deliverysystem 230 and the delivery system 230 may return the execution resultsof these actions.

The embodiments disclosed herein may include or be implemented inconjunction with an artificial reality system. Artificial reality is aform of reality that has been adjusted in some manner beforepresentation to a user, which may include, e.g., a virtual reality (VR),an augmented reality (AR), a mixed reality (MR), extended reality (XR),a hybrid reality, or some combination and/or derivatives thereof.Artificial reality content may include completely generated content orgenerated content combined with captured content (e.g., real-worldphotographs). The artificial reality content may include video, audio,haptic feedback, or some combination thereof, and any of which may bepresented in a single channel or in multiple channels (such as stereovideo that produces a three-dimensional effect to the viewer).Additionally, in some embodiments, artificial reality may be associatedwith applications, products, accessories, services, or some combinationthereof, that are, e.g., used to create content in an artificial realityand/or used in (e.g., perform activities in) an artificial reality. Theartificial reality system that provides the artificial reality contentmay be implemented on various platforms, including a head-mounteddisplay (HMD) connected to a host computer system, a standalone HMD, amobile device or computing system, or any other hardware platformcapable of providing artificial reality content to one or more viewers.

Artificial/Augmented Reality Systems

FIG. 5A illustrates an example artificial reality (AR) system 500A. Inparticular embodiments, the artificial reality system 500 may comprise aheadset 504, a controller 506, and a computing system 508. A user 502may wear the headset 504 that may display visual artificial realitycontent to the user 502. The headset 504 may include an audio devicethat may provide audio artificial reality content to the user 502. Theheadset 504 may include one or more cameras which can capture images andvideos of environments. The headset 504 may include an eye trackingsystem to determine the vergence distance of the user 502. The headset504 may be referred as a head-mounted display (HDM). The controller 506may comprise a trackpad and one or more buttons. The controller 506 mayreceive inputs from the user 502 and relay the inputs to the computingsystem 508. The controller 206 may also provide haptic feedback to theuser 502. The computing system 508 may be connected to the headset 504and the controller 506 through cables or wireless connections. Thecomputing system 508 may control the headset 504 and the controller 506to provide the artificial reality content to and receive inputs from theuser 502. The computing system 508 may be a standalone host computersystem, an on-board computer system integrated with the headset 504, amobile device, or any other hardware platform capable of providingartificial reality content to and receiving inputs from the user 502.

FIG. 5B illustrates an example augmented reality (AR) system 500B. Theaugmented reality system 500B may include a head-mounted display (HMD)510 (e.g., glasses) comprising a frame 512, one or more displays 514,and a computing system 520. The displays 514 may be transparent ortranslucent allowing a user wearing the HMD 510 to look through thedisplays 514 to see the real world and displaying visual artificialreality content to the user at the same time. The HMD 510 may include anaudio device that may provide audio artificial reality content to users.The HMD 510 may include one or more cameras which can capture images andvideos of environments. The HMD 510 may include an eye tracking systemto track the vergence movement of the user wearing the HMD 510. Theaugmented reality system 500B may further include a controllercomprising a trackpad and one or more buttons. The controller mayreceive inputs from users and relay the inputs to the computing system520. The controller may also provide haptic feedback to users. Thecomputing system 520 may be connected to the HMD 510 and the controllerthrough cables or wireless connections. The computing system 520 maycontrol the HMD 510 and the controller to provide the augmented realitycontent to and receive inputs from users. The computing system 520 maybe a standalone host computer system, an on-board computer systemintegrated with the HMD 510, a mobile device, or any other hardwareplatform capable of providing artificial reality content to andreceiving inputs from users.

Voice Command Integration

In particular embodiments, a client system may implement voice commandswithin an extended reality (XR) environment (e.g., augmented reality(AR) or virtual reality (VR)) via a voice SDK, which allows XRapplications to easily integrate voice commands on XR devices (e.g., theclient system). In particular embodiments, the client system mayimplement voice commands in combination with gestures within XRenvironments via a voice SDK. As XR content becomes more immersive,voice integration may make the content more engaging to interact withvarious applications. That is, people typically interact with the realworld using their voice and hands. Currently, navigating through XRcontent may be cumbersome as the user may need to navigate throughunfamiliar nested menus. As an example and not by way of limitation, auser may need to click through various virtual menus by ray-casting froma controller or inputting text through a virtual keyboard to navigate toa desired destination. To improve upon the user experience, the XRsystem may implement voice commands, combined with one or more othermodalities (e.g., gesture, pose, eye gaze, etc.) to allow a user toperform voice navigation/search, voice FAQ, and voice-driven gameplay &experiences.

In particular embodiments, in order to implement voice commands into XRcontent, a voice SDK may be used by application developers to customizevoice commands for their applications. In particular embodiments, thevoice SDK may provide various features for applications, such ascapturing voice requests with out-of-the-box activation methods,automatic speech recognition (ASR) for a user's utterance, receive liveASR transcription for a spoken utterance, built-in intents, entities,and traits with no natural language understanding (NLU) training,training custom NLU capabilities specific for applications, and improvevoice request accuracy with dynamic entities. In particular embodiments,the voice SDK may also be complimented with a natural-language platformto process voice requests into user intents to trigger relevantapplication functionalities. A typical voice command flow may start withreceiving a user input to trigger an initiation to receive a voicecommand. The user input may include one or more of a button press,clicking a user interface element, a gesture, and the like. This userinput may place the XR system into a listening mode for a voice command.There may be other activation modalities including head pose, headgesture, eye gaze, voice, proximity, touch, and the like. The differentactivation modalities enable voice-driven interactions andmulti-sequence activation. As an example and not by way of limitation,voice-driven interactions may include interactions with non-playercharacters, gameplay that requires voice activation for certainelements, and the like. The activation methods may also improveaccessibility, provide greater flexibility in physical interactions withXR objects (e.g., non-player characters), and preserve immersion byreducing the disruption (e.g., needing to pull up a menu to select anoption or the like) to the flow of an application experience. To avoidunintentional activation, another modality may be used in conjunctionwith gesture activation (e.g., voice, gaze, etc.). As an example and notby way of limitation, a user can gaze at an object to select it (and theattention system indicates the object is ready to receive input), andthen when the user makes a subsequent input (voice or gesture or both),the system knows the command is directed at the object in the user'sgaze. As another example and not by way of limitation, a user candirectly touch an object (real or virtual) to select the object,allowing subsequent voice interactions with respect to the object. Asanother example and not by way of limitation, the user can change theirposition with respect to an object to select it (e.g., move towards anobject) or take a certain pose to initiate the system (e.g., tiltingyour head at a certain angle to wake the system), allow subsequentvoice/gesture inputs. Multi-sequence activation can be used to allowcertain sequences of various modalities of inputs in any order to causeactivation, such as using a gesture and then voice activation. The XRsystem may then receive an audio input from the user through one or moremicrophones corresponding to the voice command. The XR system may thenprocess the audio input to identify one or more user intents andentities. The XR system may use the application that received the audioinput in order to perform a voice command based on the identified one ormore user intents and entities. As an example and not by way oflimitation, the developer of the application may add a voice command tosearch for workouts on their application. The user input may be “Show mea 10 minute boxing workout”, which may be processed using a NLU model toidentify an intent: start_workout, entity−[duration]=10 minute, entity[workout_category]=boxing. These intents and entities may be sent to theapplication to perform application functionality corresponding to theintents and entities.

In particular embodiments, an assistant system or one or more componentsof the assistant system may be used to help process voice commands. Asan example and not by way of limitation, the user may start an audioinput with “hey assistant” to initiate a voice command. The audio inputmay include a wake word to place the XR system into a listening mode forthe voice command. The voice commands may include voice destinations,content search, hands-free navigation and control, voice-drivengameplay, and other functionalities. Voice destinations may be a voicecommand to allow a user to jump to any registered destination usingvoice. Content search may enable a user to quickly search forinformation associated with an application using a voice command.Hands-free navigation and control may include a voice command to allow auser to invite a friend into a gaming lobby among other things. Thevoice-driven gameplay may implement voice commands that are integratedin the gameplay of particular applications. As an example and not by wayof limitation, a wizards duel game may implement the casting of wizardspells through a combination of gesture and voice input. For instance, auser may need to say a specific phrase and perform a specific hand waveto complete the voice command corresponding to a particular spell.Gesture detection may be done using, for example, one or moreaccelerometers in hand-held controllers, electromyography (EMG) signalsfrom a wearable wristband, vision-based hand tracking techniques (formodeling the position and movement of the user's hands), or othersuitable techniques. The detection of the gesture may place the XRsystem in a listening mode for a particular application or theapplication may be set in a listening mode for certain contexts. Forinstance, if the gameplay requires audio input from the user, then theXR system may be set to a listening mode for the certain contexts. A XRsystem may use a language model to identify phrases from an audio inputand compare that to a list of phrases associated with the application.The XR system may identify a corresponding gesture performed and comparethat to a list associated with a combination of phrases and gestures.When a particular phrase and gesture combination is detected, then theXR system may present a corresponding output, such as a resulting wizardspell. The XR system may perform object recognition on real-worldobjects and allow the user to interact with the object (e.g., see areal-world menu, CV recognizes it, then point at an item on the menu andask the assistant to tell you about it).

In particular embodiments, an application developer may initially set upa list of actions to perform based on voice commands. The developer mayspecify an utterance, a phrase, voice command, etc. that is linked to anintent. The developer may identify an n-gram within the utterance,phrase, voice command, etc. and generate an entity based on theidentified n-gram. The NLU model may identify synonyms corresponding tothe n-gram associated with an entity, such that when a user speaks anutterance or phrase, the XR system would perform the same actionassociated with the original intent and entity combination. Inparticular embodiments, a voice SDK may include a set of intents andentities available to be used in voice commands.

In particular embodiments, the voice SDK may enable voice interactionswith applications. These voice interactions may include voice commandsthat consolidate controller actions into a single phrase or interactiveconversation that makes an application more engaging. In particularembodiments, the voice SDK may capture voice requests with variousactivation method, access ASR for a user's utterance, receive live ASRtranscription for the spoken utterance, use one or more includedintents, entities, and traits, train custom NLU capabilities specificfor a particular application, and improve voice request accuracy usingsynonyms for entities.

In particular embodiments, the voice commands may be used for voicenavigation and search, voice FAQ, and voice-driven gameplay andexperiences. In particular embodiments, the voice SDK implemented by aclient system may have built-in intents, entities, traits, with supportfor full customization. As an example and not by way of limitation, anapplication developer may start with a built-in intent, entity, trait,etc. and customize the intent to fit their application. In particularembodiments, the voice SDK may have cross-platform support to apply toother devices. As an example and not by way of limitation, if a voicecommand was created for a particular XR system, the voice command may beused for other XR systems. In particular embodiments, the voice SDK mayfunction in a plurality of different languages.

In particular embodiments, a client system may alter one or moreelements of an XR environment in response to a voice command receivedfrom a user. As an example and not by way of limitation, the user maysay “change the desk to red”. The client system may use one or moremethods to confirm the voice command as described herein. As an exampleand not by way of limitation, the client system may generate an XRelement pointing to the identified entity (e.g., the desk) to confirmwhich element the user is directed to. While a singular element isdiscussed, the user may alter multiple elements or the entire XRenvironment using voice commands. As an example and not by way oflimitation, the user may request to return to a home environment of anXR system, which would alter the entire XR environment.

In particular embodiments, the voice interactions a user of a clientsystem may experience may factor in one or more of language used, tone,context, visuals, gestures, facial expressions, and body language. Thevoice interactions may be interactive, contextual, cooperative,variable, and multimodal.

In particular embodiments, the client system may include severalactivation models to input a voice command or invoke a voiceinteraction. The activation models may include UI affordance, immersion,gaze, gesture, and an attention system. In particular embodiments, theUI affordance may be an option within a user interface that can be usedto activate a voice interaction. The voice interaction may be settingthe client system into a listening mode to receive a voice command,begin an audio interaction, and other voice interactions. The userinterface that a user may interact with may be embodied as a mic button,exclamation point above a character's head, and the like. The userinterface may include keyboard dictation or search activities. UIaffordance may be easily transferable to other devices. In particularembodiments activation of a voice interaction using UI affordance mayinclude a controller click on a microphone button, a hand pinch click onUI button, a hand direct touch on UI button.

In particular embodiments, an activation model including immersion maybe an option that is effective and natural when used within a context ofa game. As an example and not by way of limitation, a user may rub amagic lamp which activates a voice interaction, the user may stand infront of a magic mirror, or the user may talk to a non-player character(NPC). The immersion activation model may be used for voice-drivengameplay.

In particular embodiments, an activation model including gaze may be anoption that uses the first-person perspective an eye tracking as anactivation model. In particular embodiments, the client system mayperform eye tracking to determine a user's gaze (after receiving userpermission). The gaze activation model may be combined with othergestures or game elements to provide an immersive experience. As anexample and not by way of limitation, a user gazing at a non-playercharacter and waving to speak and start a voice interaction. As anotherexample and not by way of limitation, a user may lock onto a targetduring gameplay to use a voice command.

In particular embodiments, an activation model including gestures may bean option for voice-driven gameplay. Gesture activation may be combinedwith head/eye gaze or used separately. As an example and not by way oflimitation, the user may make a gesture such as with a wand. Inparticular embodiments, the gesture activation may be used formultisequence activation. The gesture activation may be combined with avoice input.

In particular embodiments, an activation model may include mechanismsusing an attention system. Natural attention indicators and modalitiesmay be used for activation of a voice interaction. As an example and notby way of limitation, body language and gaze.

In particular embodiments, a client system may receive a user input toplace the client system in a listening mode. In particular embodiments,the client system may be embodied as an XR display device. The userinput may comprise one or more of a button press, wake word, gestureinput, and the like. The client system may be configured to receive anaudio input and any additional inputs in response to the user input. Inparticular embodiments, the client system may receive a multimodal inputcomprising (1) an audio input comprising a voice command and (2) agesture input corresponding to the voice command. In particularembodiments, the gesture input may correspond to the user input to placethe client system in a listening mode. In particular embodiments, theclient system may process, using a natural-language model, the audioinput to identify one or more intents and one or more entitiesassociated with the voice command. In particular embodiments, the clientsystem may use the natural-language model of the assistant system toprocess the audio input. In particular embodiments, the client systemmay determine a gesture performed based on the gesture input. As anexample and not by way of limitation, the client system may determine acircle gesture was performed based on the gesture input. The gestureinput may be received at one or more hand-held controllers associatedwith the client system. In particular embodiments, the client system mayexecute an action based on the identified one or more intents, theidentified one or more entities, and the gesture. As an example and notby way of limitation, within a wizard dueling application, the actionmay be to display a spell animation.

In particular embodiments, an extended reality (XR) display device mayreceive a gesture-based input from a first user of the XR displaydevice. The XR display device may be embodied as the client system. Inparticular embodiments, the XR display device may use one or moresensors or a device coupled to the XR display device to receive agesture-based input from the first user. As an example and not by way oflimitation, the XR display device may receive a gesture-based input(e.g., a circle gesture) from a controller coupled (wired or wireless)to the XR display device. In particular embodiments, the XR displaydevice may receive a tertiary input, wherein the tertiary input maycomprise one or more of a touch input, a gaze input, or a pose input. Asan example and not by way of limitation, the XR display device may useone or more cameras to capture the gaze input. Although this disclosuredescribes receiving an input from a user in a particular manner, thisdisclosure contemplates receiving an input from a user in any suitablemanner.

In particular embodiments, the XR display device may process, using agesture-detection model, the gesture-based input to identify a firstgesture. In particular embodiments, the XR display device may use agesture-detection model that was trained to detect gestures made byusers of the XR display devices. As an example and not by way oflimitation, the gesture-detection model may be trained to detectgestures that a user may make while using the XR display device, such asa circle gesture, a pointing gesture, and the like. Although thisdisclosure describes processing a gesture-based input in a particularmanner, this disclosure contemplates processing a gesture-based input inany suitable manner.

In particular embodiments, the XR display device may receive an audioinput from the first user, wherein the audio input may comprise a firstvoice command. In particular embodiments, the XR display device may useone or more microphones to receive the audio input from the first user.In particular embodiments, the XR display device may place one or moremicrophones of the XR display device into a listening mode responsive toidentifying the first gesture. In particular embodiments, the XR displaydevice may place one or more microphones of the XR display device into alistening mode responsive to receiving a tertiary input, such as a gazeinput. As an example and not by way of limitation, the XR display devicemay place one or more microphones of the XR display device into alistening mode if a user looks at an object. Although this disclosuredescribes receiving an audio input in a particular manner, thisdisclosure contemplates receiving an audio input in any suitable manner.

In particular embodiments, the XR display device may process, using anatural language understanding (NLU) model, the audio input to identifyone or more intents or one or more slots associated with the first voicecommand. In particular embodiments, the XR display device may processthe audio input responsive to both identifying a first gesture andreceiving the audio input from the first user. As an example and not byway of limitation, the XR display device may process an audio input whena user simultaneously makes a gesture and says an audio input at thesame time. Although this disclosure describes processing an audio inputin a particular manner, this disclosure contemplates processing an audioinput in any suitable manner.

In particular embodiments, the XR display device may determine whetherthe identified first gesture matches the first voice command. Inparticular embodiments, the XR display device may determine whether avoice command matches a gesture made by the user. As an example and notby way of limitation, the XR display device may determine whether thevoice command “fireball” matches a circle gesture made by the user. Inparticular embodiments, the XR display device may determined whether oneor more tertiary inputs also or instead matches the first voice command.As an example and not by way of limitation, the XR display device maydetermine whether a gaze input matches the voice command a user said.Although this disclosure describes determining whether an identifiedgesture matches a voice command in a particular manner, this disclosurecontemplates determining whether an identified gesture matches a voicecommand in any suitable manner.

In particular embodiments, the XR display device may execute a firsttask corresponding to the first voice command based on the identifiedfirst gesture and the identified one or more intents or one or moreslots. In particular embodiments, the XR display device may execute,responsive to the identified first gesture matching the first voicecommand, the first task corresponding to the first voice command basedon the identified first gesture and the identified one or more intentsor one or more slots. In particular embodiments, the XR display devicemay execute the first task corresponding to the first voice commandbased on one or more other inputs (e.g., tertiary input, such as gazeinput) and the identified one or more intents or one or more slots. Inparticular embodiments, the executing of the first task may further bebased on an order of receiving the Although this disclosure describesexecuting a first task corresponding to a first voice command in aparticular manner, this disclosure contemplates executing a first taskcorresponding to a first voice command in any suitable manner.

In particular embodiments, the XR display device may render visualfeedback responsive to executing the first task. In particularembodiments, the XR display device may render visual feedback on one ormore displays of the XR display device. As an example and not by way oflimitation, if the first task comprises generating a fireball visual,the XR display device may render a fireball while the XR display deviceis executing the first task. In particular embodiments, the XR displaydevice may render, for one or more displays of the XR display device, auser interface comprising a menu of one or more activatable userinterface elements responsive to executing the first task. As an exampleand not by way of limitation, the XR display device may render anavigation menu to access information and/or other parts of theapplication associated with the XR environment the user is located. Inparticular embodiments, the XR display device may render, for one ormore displays of the XR display device, information corresponding tofrequently asked questions responsive to executing the first task.Although this disclosure describes rendering one or more elements in aparticular manner, this disclosure contemplates rendering one or moreelements in any suitable manner.

In particular embodiments, the XR display device may capture one or moreimages corresponding to a real-world environment of the first user. Inparticular embodiments, the XR display device may use one or morecameras of the XR display device to capture one or more imagescorresponding to the real-world environment of the first user. As anexample and not by way of limitation, the one or more cameras of the XRdisplay device may capture passthrough images for the user to view. Inparticular embodiments, the XR display device may be an augmentedreality device that comprises lens that the user may see through. Inparticular embodiments, the XR display device may process, using amachine-learning model, the one or more images to identify one or morereal-world objects within the one or more images. As an example and notby way of limitation, the XR display device may use a machine-learningmodel to identify a menu of a restaurant within the one or more images.Although this disclosure describes capturing one or more images in aparticular manner, this disclosure contemplates capturing one or moreimages in any suitable manner.

In particular embodiments, the XR display device may analyze one or moreof the identified real-world objects to perform the first task. Inparticular embodiments, the first task may comprise identifying textcorresponding to one or more real-world objects responsive to analyzingthe one or more identified real-world objects. The text may be in afirst language. The first task may comprise translating the text fromthe first language to a second language. The first task may alsocomprise presenting, by the XR display device, the translated text tothe first user. As an example and not by way of limitation, the XRdisplay device may present the translated text through rendering thetext to be displayed on one or more displays of the XR display device orthrough outputting an audio output through speakers of the XR displaydevice. In particular embodiments, the XR display device may identifyone or more online media corresponding to the one or more real-worldobjects responsive to analyzing the one or more identified real-worldobjects. As an example and not by way of limitation, the XR displaydevice may identify a website associated with a menu identified in thereal-world environment. The XR display device may render a userinterface containing the website on one or more displays of the XRdisplay device. Although this disclosure describes analyzing the one ormore identified real-world objects in a particular manner, thisdisclosure contemplates analyzing the one or more identified real-worldobjects in any suitable manner.

FIGS. 6A-6B illustrate an example flow diagram of processing an audioinput. The process 600 may be performed by a client system as describedherein. The client system may be embodied as an XR system. In particularembodiments, the process 600 may start at step 602, where a clientsystem receives an utterance from a user. At step 604, the client systemmay use ASR and a natural language model to process the utterance. Theclient system may process the utterance to generate one or more intentsand one or more entities. The client system may determine whether it wasable to understand the request. If the client system is unable tounderstand the request, the process 600 continues to step 606 where ageneric error response 608 is outputted to the user. In particularembodiments, the client system may partially understand the request orhas a low confidence in understanding the request. In particularembodiments, if the client system partially understands the request orhas low confidence in understanding the request, the client system maydetermine whether the client system is able to handle any of theidentified intents or slots at step 610. In particular embodiments, theclient system may generate a request to have the user repeat themselves.In particular embodiments, the client system may confirm what the useris attempting to request. In particular embodiments, the client systemmay present an audio output to the user to respond to the userutterance. If the client system is able to understand the request, theprocess 600 may continue to step 610, where the client system determineswhether the client system has the ability to handle the request. As anexample and not by way of limitation, the client system may attempt tocomplete the request to determine whether the client system is able tohandle the request. If the client system is unable to handle therequest, the process 600 continues to step 612 where the client systemoutput one or more error responses 614 to the user. If the client systemis able to handle the request, the process 600 continues to step 616where the client system determines whether the client system has all theinformation needed to perform and action or respond to the user request.If the client system does not have all the information needed, then theprocess 600 continues to step 618 where the client system may outputfollow up questions 620 to request further information from the user. Ifthe client system does have all the information, the client system mayproceed to step 622 (shown in FIG. 6B) to perform an action to respondto the user utterance. The process 600 continues to a response step 624,628, 632, 636, 640, or 644 based on the identified intents and entitiesin the user utterance. The process 600 can continue to step 624 torespond with one or more confirmations 626. The process 600 can continueto step 628 to respond with one or more lists 630. The process 600 cancontinue to step 632 to respond with one or more media actions 634. Theprocess 600 can continue to step 636 to respond with one or more answersto questions 638. The process 600 can continue to step 640 to respondwith one or more device setting updates 642. The process 600 cancontinue to step 644 to perform another type of action or response tothe user utterance.

FIG. 7 illustrates an example flow diagram of processing an audio input.In particular embodiments, a client system 702 (e.g., client system 130)may be embodied as a XR display device, such as an AR headset or a VRheadset. In particular embodiments, the client system 702 may performone or more processes as described herein. The client system 702 mayinterface the assistant system 704 (e.g., assistant system 140). Inparticular embodiments, the assistant system 704 may comprise one ormore computing systems. In particular embodiments, the assistant system704 may be embodied as an NLP tool to process audio inputs. Inparticular embodiments, the client system 702 may have an application706 installed on it. The application 706 may be used to generate andrender XR content (e.g., elements and/or environment). In particularembodiments the application 706 may specify one or more activationmethods for triggering reception of audio inputs. As an example and notby way of limitation, a user may trigger an invocation model to place anNPC in an XR environment into a listening mode. In particularembodiments, the application 706 may send a request to the assistantservice 708 to place one or more microphones of the client system 702into a listening mode. In particular embodiments, the assistant service708 may communicate with the operating system 710 to determine whetherthe application 706 has permission to access the microphone. In responseto the operating system 710 determining that the application 706 hasaccess, the operating system 710 may open access to the application 706.In particular embodiments, the application 706 may request to stream theaudio inputs received to the assistant system 704 to process. Inparticular embodiments, the assistant system 704 may use an NLP tool toprocess any received audio inputs. In particular embodiments, theassistant service 708 may stream audio inputs received from one or moremicrophones of the client system 702 to the speech recognition model 712of the assistant system 704. In particular embodiments, the speechrecognition model 712 may transcribe the audio received to text. Thespeech recognition model 712 may send the transcribed text to thenatural language understanding model 714. The NLU model may match thetext to intent and extract entities for the slots. In particularembodiments, the NLU model 714 may send the results back to theapplication 706. In particular embodiments, the NLU model 714 may sendthe intents, slots, and entities to a dialog manager 716. The dialogmanager 716 may resolve the intents and slots received from the NLUmodel. In particular embodiments, the assistant system 704 may perform atask or send instructions to execute a task to the client system 702.The dialog manager 716 may send instructions to the application 706 toperform a task based on the received intents, slots, and entities fromthe NLU model. In particular embodiments, the assistant system 704 maygenerate a result from processing the intents, slots, and entities fromthe NLU model 714 and render a response through the text-to-speechmodule 718. The response from the text-to-speech module 718 may send theresponse to the application 706 to output to the user of the clientsystem 702. As an example and not by way of limitation, if the userasked an NPC in an XR environment associated with the application 706“What do you have for sale?” the text-to-speech module 718 may generatea response going through this process and have the application 706output the response, “I have baked goods for sale. Would you like to buysome?” In particular embodiments, the client system 702 may generate theresponse through the application 706 by receiving the intents, slots,and entities from the NLU model 714.

FIG. 8 illustrates an example architecture of a system 800 to process auser input. In particular embodiments, the system 800 may include anengine 802. The engine 802 can be embodied as a game engine. Inparticular embodiments the engine 802 may interact with the platformservice 804 through a process boundary 814. In particular embodiments,the platform service 804 may be embodied as an assistant system 140. Inparticular embodiments, the engine 802 may include a toolkit 804, one ormore applications 808, a voice SDK 812, engine application triggers 824,and engine application callbacks 858. In particular embodiments, theplatform service 804 may include the voice SDK service process 816. Inparticular embodiments, the application 808 may initially render an XRenvironment or XR elements, such as object 818. In particularembodiments, the application 808 may determine the context of a user ofthe application 808. The application 808 may determine the context ofthe user with respect to one or more objects 818 within the XRenvironment. In particular embodiments, the application 808 may access atoolkit 804 to provide additional functionality to the XR environment.In particular embodiments, the object 818 may send information, such aslocation of the user with respect to the object 818, location of theobject, context of the application 808, and the like to the invocationmodule 820. In particular embodiments, the invocation module 820 maydetermine whether to activate an attention system based on parameters(e.g., user gaze, distance, and the like) received from the object 818and as described herein. In particular embodiments, if the invocationmodule 820 determines to activate an attention system, the invocationmodule 820 may send a request to the attention system module 822 tochange an attention state of the object 818. In particular embodiments,the toolkit 804 may track and manage the attention systems and attentionstates of multiple objects 818 across different applications 808. Inparticular embodiments, the attention system module 822 may sendinstructions to the application 808 to render a different form of theobject 818 to reflect a different attention state. As an example and notby way of limitation, if the object 818 is a virtual cat initially in anapping position. As a user of the application 808 approaches the object818, the attention system module 822 may send instructions to theapplication 808 to render the virtual cat into a microphone on attentionstate, where the virtual cat can change into a sitting upright position.In particular embodiments, the application 808 may receive audio inputsfrom the user of the application 808. In response to receiving an audioinput, the application 808 may send a signal to the engine applicationtriggers 824 to process the audio input. In particular embodiments, theengine application triggers may specify certain conditions to triggerprocessing an audio input as described herein. As an example and not byway of limitation, the user of the application 808 may need to perform agesture and then say an audio input. In particular embodiments, theengine application triggers 824 may send the audio input from theapplication 808 to the voice SDK 812. In particular embodiments, thevoice SDK 812 determines whether to be activated at step 826 to processan audio input through signals received from the engine applicationtriggers 824 and the toolkit 804.

In particular embodiments, the voice SDK 812 may use step 826 todetermine to process an audio input. At step 828, the voice SDK 812 maydetermine whether or not the platform of the client system running theapplication 808 is supported. The voice SDK 812 may provide additionalfeatures to processing an audio input as described herein. As an exampleand not by way of limitation, the voice SDK 812 may quickly processaudio inputs using customized on-device NLU models. In particularembodiments, if the voice SDK 812 determines that the platform is notsupported, then the audio input may be sent to an NLP tool native 835.The NLP tool native may activate a microphone input 850 at step 848. Themicrophone input 850 may call on an API 854 to process the audio input.In particular embodiments, the API 854 may be to call on an NLP tool toprocess the audio input. In particular embodiments, the API 854 may sendback the results of the NLP tool to the response handler 852. Inparticular embodiments, the response handler 852 may send the responseto a voice SDK response handler 856. If the voice SDK 812 determinesthat the platform is supported 828, then the voice SDK platformintegration 830 may be activated at step 832. In particular embodiments,only certain client systems may be equipped with the right hardware toimplement certain features of the voice SDK. The audio input may bepassed to the voice SDK service process 816 by the voice SDK integration830 through the process boundary 814. The voice SDK service process 816may determine whether the application 808 and/or the client systemrunning the application 808 may have permission to use the voice SDKservice process 816. If the application 808 and/or client system doesnot have permission, then the voice SDK service process 916 may pass theaudio input back to the NLP tool native 835 to handle the audio input.In particular embodiments, if the voice SDK service process 816determines that the application 808 and/or client system has permission,then the audio input gets passed to the NLP platform integration tool836. In particular embodiments, the voice SDK service process 816 mayactivate the NLP platform integration tool 836 at step 838. The audioinput may be passed to an OVR microphone input 840. The OVR microphoneinput 840 may pass the audio input to an API 844. In particularembodiments, the API 844 may call an NLP tool to process the audioinput. In particular embodiments, the result of the NLP tool may bepassed to a response handler 842 of the NLP platform integration tool836. In particular embodiments, the response handler 842 may send theresponse through the process boundary 814 to the platform SDK 846 of thevoice SDK platform integration 830. In particular embodiments, theresults of the response handler 852 and the platform SDK 846 may be sentto the voice SDK response handler 856. In particular embodiments, thevoice SDK response handler 856 may generate a response to send back tothe application 808 or the toolkit 804. In particular embodiments, thevoice SDK response handler 856 may send a response to the engineapplication callbacks 858 that may activate the attention system module822 or the application 808 as needed. As an example and not by way oflimitation, if the user of the application 808 had called out an NPC inthe XR environment, the voice SDK response handler 856 may determine tochange an attention state of the NPC using the attention system module822 and to render a response to the user through the application 808.The engine application callbacks 858 may call both the toolkit and theapplication 808.

FIG. 9 illustrates an example method 900 for processing an audio input.A client system 130 embodied as an augmented reality headset or virtualreality headset may perform the method 900. The method may begin at step910, where the client system may receive a user input to place theclient system into a listening mode. At step 920, the client system mayreceive a multimodal input comprising (1) an audio input comprising avoice command and (2) a gesture input corresponding to the voicecommand. At step 930, the client system may process, using anatural-language model, the audio input to identify one or more intentsand one or more entities associated with the voice command. At step 940,client system may determine a gesture performed based on the gestureinput. At step 950, client system may execute an action based on theidentified one or more intents, the identified one or more entities, andthe gesture. Particular embodiments may repeat one or more steps of themethod of FIG. 9 , where appropriate. Although this disclosure describesand illustrates particular steps of the method of FIG. 9 as occurring ina particular order, this disclosure contemplates any suitable steps ofthe method of FIG. 9 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forprocessing an audio input including the particular steps of the methodof FIG. 9 , this disclosure contemplates any suitable method forprocessing an audio input including any suitable steps, which mayinclude all, some, or none of the steps of the method of FIG. 9 , whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 9 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 9 .

FIG. 10 illustrates an example XR environment 1000 of an application. Inparticular embodiments, the XR environment 1000 may represent a wizarddueling game application environment. In particular embodiments, theuser represented by the avatar 1002 may be one competitor in the gameapplication. In particular embodiments, the client system (e.g., XRdisplay device) that is rendering the environment 1000 may process audioinputs and other inputs from the user to generate and render elements1004 a-1004 c of the environment 1000. In particular embodiments, theuser may be required to perform a gesture 1006 to place one or moremicrophones of the client system into a listening mode to receive anaudio input to perform one or more tasks. As an example and not by wayof limitation, to conjure a spell, the user may need to perform thegesture 1006 to initiate a listening mode of the microphones of theclient system. The gesture 1006 may be reflected by the avatar 1002. Theuser may then say an audio input to conjure a spell. The application mayprocess the audio input using a voice SDK to determine whether the audioinput comprises a voice command as described herein. The application mayprocess the audio input and render an effect as a result of processingthe audio input. In particular embodiments, the user may wish to performa card spell. The user may perform the gesture 1006 and say an audioinput that includes a voice command to perform the card spell. As aresult, the application may render the elements 1004 a-1004 c to reflectthe successful casting of the card spell. In particular embodiments, theuser may need to perform the gesture while simultaneously saying thevoice command to perform the card spell. In particular embodiments, theapplication may require the user to input one or more inputs (in asequence or simultaneously) to perform a voice command.

FIG. 11 illustrates an example method 1100 for processing a voicecommand. A client system 130 embodied as an augmented reality headset,virtual reality headset, or extended reality headset may perform themethod 1100. The method may begin at step 1110, where the client systemmay receive a gesture-based input from a first user of the clientsystem. At step 1120, the client system may process, using agesture-detection model, the gesture-based input to identify a firstgesture. At step 1130, the client system may receive an audio input fromthe first user. In particular embodiments, the audio input comprises afirst voice command. At step 1140, client system may process, using anatural-language model, the audio input to identify one or more intentsor one or more slots associated with the first voice command. At step1150, client system may determine whether the identified first gesturematches the first voice command. At step 1160, client system mayexecute, responsive to the identified first gesture matching the firstvoice command and by the XR display device, a first task correspondingto the first voice command based on the identified first gesture and theidentified one or more intents or one or more slots. Particularembodiments may repeat one or more steps of the method of FIG. 11 ,where appropriate. Although this disclosure describes and illustratesparticular steps of the method of FIG. 11 as occurring in a particularorder, this disclosure contemplates any suitable steps of the method ofFIG. 11 occurring in any suitable order. Moreover, although thisdisclosure describes and illustrates an example method for processing avoice command including the particular steps of the method of FIG. 11 ,this disclosure contemplates any suitable method for processing a voicecommand including any suitable steps, which may include all, some, ornone of the steps of the method of FIG. 11 , where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method of FIG. 11 , this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method of FIG. 11 .

Attention System

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). In particular embodiments, a client system may implement anattention system to provide audio-visual cues to a microphone status inXR environments. There are sometimes issues with users identifying andunderstanding which entities/objects are interactable by voice within anXR environment. Additionally, users sometimes are not knowledgeable onwhat voice commands are available to them for certain contexts, such asfor assistant experiences on voice-forward and voice-only devices andimmersive in-app experiences in XR. To help the user distinguish whichobjects are interactable via voice command, an attention system may beused to provide audio-visual cues that let users know various attentionstates of a XR object, such as when the XR object is ready to receive avoice command, when the microphone is active, and when the system isprocessing the voice command.

In particular embodiments, the attention system may use designcomponents provided by the voice SDK, which may allow third-partyapplication developers to easily incorporate the attention system designinto their application. The attention system may be maintained by the XRsystem using a toolkit that includes an attention system module andinvocation module. The toolkit may integrate with the voice SDK. Theattention system may be object-based, so each object may have its ownattention state, which may be indicated visually (e.g., with an iconover the XR object). As an example, each game object in an VRenvironment, such as an interactable VR dog and an interactable VR cat,may have its own attention state and corresponding audio-visual cues.The voice SDK may include pre-built design components that allow objectsto indicate a change in attention state in response to user interactions(e.g., voice, gesture, gaze, movement, inputs) or application events toindicate, for example, that voice commands can be user to interact withthat object, that the system is processing a prior voice command, orthat the object is responding. The pre-build design components mayinclude an invocation module, manipulation module, voice-search module,and response module. The invocation module may provide predefinedinvocation methods including UI affordance, activation zone (withdistance tolerance), gaze, time based, gesture, or custom wake word. Theinvocation module may be used to determine whether to activate theattention system and change an attention state indicator for an object.As an example and not by way of limitation, an attention system may beactivated (e.g., audio-visual cues may indicate an active attentionstate for the object) when a user is within a threshold distance (i.e.,activation zone indicates the threshold distance to activate theattention system). The manipulation module may pack basic manipulationvoice commands together so application developers can easily apply thismodule to an existing object within an application. The voice-searchmodule may enable a user to use their voice to find the match data. Asan example and not by way of limitation, the user may perform a searchwithin an XR environment using the voice-search module. The responsemodule may allow developers to dynamically register different intents ondifferent objects and return simplified response callbacks.

In particular embodiments, the attention system may provide audio-visualcues to indicate to users the system's current state for receiving voiceinputs, on an object-by-object basis, for each XR object within anenvironment. The understanding of the current attention state may beboth at a system-level and an object-level. The attention system mayprovide audio-visual feedback (i.e., attention state indicators) as tohow the user's voice commands are received, including reducing theperception of latency, responding to errors, and indicating that thesystem is receiving audio input. The attention state indicators may alsohelp users know when the microphone is “active” and “listening”, so theusers don't feel as if the system is spying on them. The attention stateindicators may include visual and/or audio indicators (e.g., highlight,icons, audio feedback, earcons) to indicate the status of the systemwith respect to a particular XR object. The attention state may includeMicOff, MicOn, Listening, Response, and Error. During a MicOff state,the microphone may be closed and voice service may be inactive withrespect to a particular XR object. The voice service may be used by theattention system to process any received audio input. When themicrophone states change to the MicOff state, the voice service may firea one-time event. During a MicOn state, the microphone may be open andvoice service may become active with respect to a particular XR object.When the microphone states change to the MicOn state, the voice servicemay fire a one-time event. The MicOn state may be activated for asystem-level or object-level attention system based on the invocationmodule. During a Listening state, the voice service may be listening foruser input with respect to a particular XR object. This listening may bea continuous event. The Listening state may pass back the listeningdata, which includes the microphone level and transcription. During theResponse state, the voice service may pass back the [NLUResponseData]which includes the timing and response content, which may be renderedwith respect to with the particular XR object the user directed a voiceinput toward. During the Error state, the voice service may pass back[ErrorData] which may include timing and response content with respectto the particular XR object the user directed a voice input toward.There may be match and non-match indicators to indicate when an XRobject in an application understands a user input vs. not understandingthe user input (but still provides a response). The match and non-matchindicators can further indicate to the user whether the user may need toprovide another user input for instance when the XR object does notunderstand the user input. An instance, in a VR adventure game, where avirtual NPC does not understand a user input may be when the user asksthe particular NPC a question that the NPC is not equipped to handle(e.g., does not understand the intents or slots associated with a userinput).

In particular embodiments, the attention system may use visual and/oraudio feedback to indicate the attention state. Application developersmay use visual cues to map with the attention system states to visuallyguide users to know when their microphone is on/off and when theattention system is ready to receive voice inputs. By visuallyclarifying when the user is able to input a voice input, the user canmore clearly understand when they are able to speak. Additionally, theattention system may also provide visual and/or audio feedback toindicate whether the attention system of an object understands the userinput. The indication of whether an object (e.g., an NPC) understands auser input can provide clarity on whether the object is able to properlyrespond to a user input. The voice SDK can provide a variety ofpre-built visual effects that can be applied in the XR environment orused to modify XR objects within the application environment. As anexample and not by way of limitation, an XR object that has an activeattention system (e.g., in a Listening state) may have a glowsurrounding the XR object. Application developers may also use audiofeedback (e.g., tones, earcons, other audio feedback) alone or inconjunction with visual feedback to indicate the microphone status. Incertain instances, the audio feedback alone can be provided to maintainimmersion. The voice SDK can provide a pre-built set of custom craftedearcon sounds that application developers may use.

In particular embodiments, a user-education module may be used to helpapplication developers teach users how to use their application. Inparticular, the user-education module may help with showing users howand when to use certain voice commands. The user-education module mayprovide contextual tips for the user based on user context orapplication state to show the user what voice commands are available atthat point. The user-education module may be separated into differentcategories (e.g., move objects, order attacks, cast spells, navigation,or actions). The voice inputs can be separated into different types ofvoice inputs, such as commands or questions. The user-education modulemay implement a dictionary that the user can access to pull up a list ofall available voice commands. The user-education module may have a helphub where the user can ask specific questions on how to perform voicecommands. As an example, for a wizard dueling application, the user canask “Hey Assistant, how do I cast the ‘fireball’ spell?” The help hubmay be customized by the developer based on the application. As anexample and not by way of limitation, for a fantasy game application,the help hub may be customized to be rendered as a spellbook. Similarlyto objects having their own attention system, each object may also havetheir own respective user education module.

In particular embodiments, the user-education module may differentiatebetween users based on characteristics. As an example and not by way oflimitation, the user-education module may differentiate between newusers and returning users. New users may have different needs withrespect to user education in comparison to returning users. As such, theuser-education module may provide different user education mechanismsfor the different users. As an example for a new user, the user may beprovided a guided walkthrough, app landing, embedded tips, and voicedictionary pop-up. As example for a returning user, the user may beprovided non-embedded tip, voice dictionary, voice dictionary pop-up,and voice search.

In particular embodiments, the attention system may use one or morecharacteristics of an XR assistant to generate motion and audio cuesthat are more animated and/or realistic to convey response and emotions.More information on rendering one or more displays of an XR displaydevice may be found in U.S. patent application Ser. No. 17/877568, filed29 Jul. 2022, which is incorporated by reference.

In particular embodiments, the assistant system 140 may presentdifferent attention states or attention substates to a use in an XRcontext. In particular embodiments, the attention system may utilize theassistant system to present different attention states or attentionsubstates. More information on presenting different attention states orattention substates to a user may be found in U.S. patent applicationSer. No. 17/934898, filed 23 Sep. 2022, which is incorporated byreference.

In particular embodiments, a client system may be embodied as an XRdisplay device. In particular embodiments, the XR display device mayrender, for one or more displays of the XR display device, a firstoutput image of an XR object within an XR environment in field of view(FOV) of a first user. As an example and not by way of limitation, theXR display device may render a virtual statue to be displayed in thefirst output image to the first user. In particular embodiments, the XRobject may be interactable by the first user. As an example and not byway of limitation, the first user may be able manipulate the XR object,such as drawing on a virtual statue. In particular embodiments, the XRobject may have a first form. As an example and not by way oflimitation, the XR object may be rendered to appear as a cat layingdown. Although this disclosure describes rendering a first output imageof an XR object in a particular manner, this disclosure contemplatesrendering a first output image of an XR object in any suitable manner.

In particular embodiments, the XR display device may detect a change ina context of the first user with respect to the XR object. As an exampleand not by way of limitation, the XR display device may detect the userapproaching the XR object. In particular embodiments, the XR displaydevice may use an invocation module to detect a change in the context ofthe first user with respect to the XR object. The invocation module mayuse one or more of UI affordance, activation zone (distance tolerance),gaze, time based, gesture, or custom wake word to detect the change incontext of the first user. Although this disclosure describes detectinga change in a context of the first user in a particular manner, thisdisclosure contemplates detecting a change in a context of the firstuser in any suitable manner.

In particular embodiments, the XR display device may determine whetherto invoke an attention system with respect to the XR object. Inparticular embodiments, the XR display device may determine whether toinvoke the attention system with respect to the XR object based on thedetected change in the context of the first user. As an example and notby way of limitation, the XR display device may detect that the changein context causes an invocation module to invoke the attention system ofan XR object. Although this disclosure describes determining whether toinvoke an attention system in a particular manner, this disclosurecontemplates determining whether to invoke an attention system in anysuitable manner.

In particular embodiments, the XR display device may render a secondoutput image of the XR object responsive to invoking the attentionsystem. In particular embodiments, the XR display device may render asecond output image of the XR object for the one or more displays of theXR display device. In particular embodiments, the XR object may bemorphed to have a second form indicating a first attention state. Thefirst attention state may indicate a status of the XR object to interactwith one or more first voice commands for one or more first functionsenabled by the XR display device. As an example and not by way oflimitation, if the XR object in its first form is a virtual cat that islaying down, the XR object in its second form may be a virtual cat thatis sitting up and looking at the first user. In particular embodiments,the XR display device may render an audio feedback indicative of the XRobject morphing to the second form for one or more speakers of the XRdisplay device. The audio feedback may correlate to the XR object or maybe a general alert sound. As an example and not by way of limitation,for a virtual cat, the audio feedback may be the virtual cat meowing asit changes to the second form. In particular embodiments, the secondoutput image may comprise an indication identifying the first attentionstate. As an example and not by way of limitation, the indication may bean open microphone icon to indicate the attention state is in amicrophone on state. Although this disclosure describes rendering asecond output image of the XR object in a particular manner, thisdisclosure contemplates rendering a second output image of the XR objectin any suitable manner.

In particular embodiments, the first attention state may comprise one ormore of a microphone on state, a microphone off state, a listeningstate, a response state, an error state, and other states. In particularembodiments, the microphone on state may indicate the XR object isactive and is ready to interact with the first user. As an example andnot by way of limitation, the microphone on state may cause the XRobject to appear in an active state, such as a virtual cat sitting up.There may be a virtual open microphone icon appearing near the virtualcat to indicate the microphone on state. In particular embodiments, themicrophone off state may indicate the XR object is not active and is notready to interact with the first user. As an example and not by way oflimitation, if the XR object is a virtual dog, the virtual dog may belaying down to represent the microphone off state attention state. Inparticular embodiments, the listening state may indicate the XR objectmay be in a continuous state of receiving audio inputs from the firstuser. As an example and not by way of limitation, if the XR object is ina virtual dog, then the virtual dog may be in a upright listeningposition to represent the listening state attention state. In particularembodiments, the response state may indicate the XR object is in a stateof outputting a response to the first user. As an example and not by wayof limitation, if the XR object is an NPC, then the NPC may outputaudible and visible dialog to respond to the first user. In particularembodiments, the error state may indicate the XR object is unable tounderstand a received input from the first user. As an example and notby way of limitation, if the XR object is a virtual dog, then thevirtual dog may be in an inquisitive state to represent the error stateattention state. Although this disclosure describes attention states ina particular manner, this disclosure contemplates attention states inany suitable manner.

In particular embodiments, the XR display device may receive an audioinput comprising a first voice command by one or more microphones of theXR display device. In particular embodiments, the XR display device mayprocess, using a natural-language understanding (NLU) model, the audioinput to identify one or more intents or one or more slots associatedwith the first voice command. In particular embodiments, the XR displaydevice may execute the first task corresponding to the first voicecommand based on the identified one or more intents or one or moreslots. Although this disclosure describes receiving an audio input in aparticular manner, this disclosure contemplates receiving an audio inputin any suitable manner.

In particular embodiments, the XR display device may render, for one ormore displays of the XR display device, a user interface overlay in theFOV of the first user. In particular embodiments, the user interfaceoverlay may comprise a search capability to search for the one or morefirst voice commands for the one or more first functions enabled by theXR display device. As an example and not by way of limitation, the userinterface overlay may be a voice search interface. In particularembodiments, the one or more first voice commands may be categorized ina plurality of different categories. The different categories maycomprise one or more of avatar movement, avatar actions, objectmovement, object actions, application settings, or system settings. Inparticular embodiments, the user interface overlay may comprise one ormore activatable user interface elements that may be selectable tosearch one or more categories of voice commands. In particularembodiments, the first voice commands may be labeled as one type of aplurality of types, wherein the different types may be commands orqueries. In particular embodiments, the one or more first voice commandsavailable for search may be based on a characteristic of the first user.As an example and not by way of limitation, the characteristic of thefirst user may comprise a user's experience with an application (e.g.,returning user vs. new user), user's age, user's preferred language, andthe like. The voice commands available for a returning user may bedifferent from the voice commands available to a new user. In particularembodiments, the one or more first voice commands available for searchmay be categorized based on one or more XR objects in the XRenvironment. As an example and not by way of limitation, the voicecommands may be categorized for a first user to select voice commandscorresponding to different XR objects in the XR environment. Inparticular embodiments, the user interface overlay may comprise a helphub to receive one or more queries from the first user. In particularembodiments, the user interface overlay may comprise an index of the oneor more first voice commands.

FIG. 12 illustrates example indications of an attention state of anobject. In particular embodiments, an application of a client system mayrender an XR environment. The XR environment may include a plurality ofXR objects. For the interactable XR objects, an indicator of theattention state may be presented in conjunction with the interactable XRobjects to provide an indication that the user of the application mayinteract with specific objects. In particular embodiments, the attentionstate indicators may include a microphone off attention state indicator1202, a microphone on attention state indicator 1204, listeningattention state indicator 1206, an error attention state indicator 1208,a response attention state indicator 1210, a mismatched understandingattention state indicator 1212, and a matched understanding attentionstate indicator 1214. In particular embodiments, one or more attentionstate indicators may be presented simultaneously for one or more XRobjects. As an example and not by way of limitation, an XR object mayhave a response attention state indicator 1210 and listening attentionstate indicator 1206 displayed at the same time. In particularembodiments, the application may determine where to render the attentionstate indicators for each interactable XR object.

FIG. 13 illustrates an example XR environment 1300 containing anattention system. In particular embodiments, the environment 1300 mayrepresent a rendered XR environment for a game application on a clientsystem. In particular embodiments, images may be rendered to one or moredisplays of the client system to output to a user. In particularembodiments, the environment 1300 may include interactable XR objects1302, 1304. In particular embodiments, the XR object 1302 may be a stoneheading and the XR object 1304 may be a door. In particular embodiments,the XR object 1302 may include text 1306. In particular embodiments, thetext 1306 may be text the user needs to say to open the door 1304. Inparticular embodiments, the XR object 1304 may include an indicator1308. In particular embodiments, the attention state of the XR objectmay change when the user approaches the XR object. The developers of thegame application may specify one or more invocation methods. As anexample and not by way of limitation, the XR object 1302 may invoke anattention system may a user approaches the XR object 1302 within athreshold distance (e.g., three meters). In particular embodiments, theattention state of the XR object 1302 may be reflected by a change incolor of the XR object. Additionally, an attention state indicator asdescribed herein may be shown in with the XR object. In particularembodiments, the indicator 1308 may be an attention state indicator. Inparticular embodiments, the indicator 1308 may specify instructions 1310for the user to follow to proceed. As an example and not by way oflimitation, as illustrated in FIG. 13 , the instructions 1310 mayinstruct the user to “try saying the magic words!”, which are inscribedabove the virtual door as “lorem ipsum dolor sit amet” . Once an audioinput is received, the application may process the audio input asdescribed herein. As an example and not by way of limitation, theapplication may use the voice SDK to process the audio input. Inparticular embodiments, once the user says the correct audio input toperform a voice command to open the door 1304, the environment 1300 maychange to reflect the processed voice command.

FIGS. 14A-14B illustrate example user interfaces 1402, 1408 of a voicedictionary. In particular embodiments, a voice SDK may provide thefunctionality to quickly search an application for voice commands thatare available to the user as described herein. In particularembodiments, the user interface 1402 may include an instruction 1404 tothe user to specify a search term. In particular embodiments, the userinterface 1402 may also include one or more activatable user interfaceelements 1406 a-1406 d that includes search terms corresponding tocategories of voice commands available for the user to search. Inparticular embodiments, the user may specify an action category of voicecommands to search and the user interface 1402 may change to the userinterface 1408. In particular embodiments, the user interface 1408 mayinclude a category 1410 and a list 1412 of actions 1414 a-1414 davailable for the user. In particular embodiments, the voice dictionaryuser interfaces 1402, 1408 may show what voice commands the user canperform. In particular embodiments, there may be other user interfacesthat the application may render in conjunction with a voice SDK toprovide user education. As an example and not by way of limitation, theapplication may also render a FAQ user interface for user review.

FIG. 15 illustrates an example method 1500 for invoking an attentionsystem of an XR object. A client system 130 embodied as an augmentedreality headset, virtual reality headset, or extended reality headsetmay perform the method 1500. The method may begin at step 1510, wherethe client system may render, for one or more displays of the clientsystem, a first output image of an XR object within an XR environment ina field of view (FOV) of a first user. In particular embodiments, the XRobject may be interactable by the first user. In particular embodiments,the XR object may have a first form. At step 1520, the client system maydetect a change in a context of the first user with respect to the XRobject. At step 1530, the client system may determine, based on thedetected change in the context of the first user, whether to invoke anattention system with respect to the XR object. At step 1540, clientsystem may render, for the one or more displays of the client system, asecond output image of the XR object responsive to invoking theattention system. In particular embodiments, the XR object is morphed tohave a second form indicating a first attention state. In particularembodiments, the first attention state indicates a status of the XRobject to interact with one or more first voice commands for one or morefirst functions enabled by the client system. Particular embodiments mayrepeat one or more steps of the method of FIG. 15 , where appropriate.Although this disclosure describes and illustrates particular steps ofthe method of FIG. 15 as occurring in a particular order, thisdisclosure contemplates any suitable steps of the method of FIG. 15occurring in any suitable order. Moreover, although this disclosuredescribes and illustrates an example method for invoking an attentionsystem including the particular steps of the method of FIG. 15 , thisdisclosure contemplates any suitable method for invoking an attentionsystem including any suitable steps, which may include all, some, ornone of the steps of the method of FIG. 15 , where appropriate.Furthermore, although this disclosure describes and illustratesparticular components, devices, or systems carrying out particular stepsof the method of FIG. 15 , this disclosure contemplates any suitablecombination of any suitable components, devices, or systems carrying outany suitable steps of the method of FIG. 15 .

On-Device Natural Language Understanding Models

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). The client system may use a customized on-devicenatural-language understanding (NLU) models to process voice inputs.Using voice commands in certain XR apps may have issues, such as latencycaused by having to send the audio file to a server, process it, andthen get a response. Even latency of a few hundred microseconds can betoo slow to make certain voice commands usable for particular apps(e.g., most action-based games). As an example and not by way oflimitation, for a fast-paced action-based game, a user may request tocall in an in-game action to be performed in the middle of an in-gameenvironment. The user may expect to have the in-game action be performedwithin a reasonable time period (e.g., a few milliseconds). To reducethe latency of processing voice commands for certain situations, thevoice SDK may enable app developers to add certain voice interactionsonto a customized on-device NLU model for the application so the voicecommands may be executed quicker. The voice SDK may enable the voiceinteractions in applications by using a pattern recognizer thatidentifies certain phrases that are expected for certain situations.

In particular embodiments, the system implementing the voice SDK (e.g.,any assistant-enabled system, such as a smartphone, XR device, smartglasses, etc.) may use customized hardware to store the customizedon-device NLU models to process voice inputs. In particular embodiments,the developers may identify certain systems (e.g., particularsmartphones or XR devices, etc.) that are equipped and enabled to usecustomized on-device NLU models for their respective applications. As anexample and not by way of limitation, certain devices may not have theprocessing capability to run an immersive VR environment as well asquickly process voice inputs in real-time. Certain functionality may bereduced for certain systems based on their technical specifications. Asan example and not by way of limitation, for smaller devices with lessprocessing power, the developers may identify a subset of voice commandsof the total amount of voice commands accessible to the typical user oftheir application to be enabled for the corresponding smaller devices.

In particular embodiments, application developers can usenatural-language processing (NLP) tool to train customized NLU modelsfor their respective applications. By training customized NLU models,the developers can enable voice commands that are executed quickly fortheir applications. The developers may provide information on aspecified voice input and the intents and slots that should correspondto that voice input. The customized NLU model may then be created andstored server-side and downloaded onto the client device when therespective application is installed. The customized NLU model may betrained to bypass the wake word and respond directly to particularwords/commands. That is voice inputs other than a wake word may berecognized and trigger in-app actions responsive to the customizedon-device NLU model. As an example and not by way of limitation, for awizard dueling game, voice inputs may trigger certain “spell casting”in-game actions. Application developers may enable an overall set oftasks with voice commands. There may be a subset of tasks set up asnormal to be executed via the normal assistant stack (i.e.,server-side). There may also be another subset of tasks set up with acustomized, compact, on-device NLU model, which enables this subset oftasks to be executed completely on the client device with minimallatency. During runtime, the system may load up a componentcorresponding to any application that is currently running so that theapplication may have its customized on-device NLU model ready to processany voice inputs.

In particular embodiments, a dual pipeline may be used to process voiceinputs (i.e., dual=client-side and server-side). A subset of quickcommands may be recognized by a customized NLU model in parallel withthe existing wake word module. The dual pipeline may use a customizedon-device NLU model trained to initially detect trigger words based on apattern-recognition model, where the customized on-device NLU model mayuse a buffer to receive the entire voice input. The pattern-recognitionmodel may be used to perform a corresponding action based on theidentified voice input. As an example and not by way of limitation, theuser may say “call in an airstrike at [in-game location]” where thepattern recognizer would identify the voice command the user isattempting to use out of a plurality of possible voice commands. Inparticular embodiments, the dual pipeline may process each voice inputso that a customized on-device NLU model may initially process a voiceinput in parallel with a server-side process on the assistant backend.After receiving the result from the server-side process, the system(e.g., client system) may use the result from the server-side process toconfirm or update the execution of any voice commands corresponding tothe voice input. As an example and not by way of limitation, if withinan application a voice command initially triggered a spell castingeffect from being processed by a customized on-device NLU model, but anassistant backend resulted in a different voice command, then the clientsystem may revise the execution of the response to a voice input. Inthis instance, a voice input may initially generate a visual effect forthe user to see and then the visual effect may transition to a nullstate or a different state when the initially perceived action is notthe intended action. These visual effects may be specified by thedevelopers for each specific in-game action, but the voice SDK enablesthe ability to apply a corrective action to an in-game action byrevising the execution of the response to a voice input.

In particular embodiments, a threshold confidence score for thecustomized on-device NLU model to trigger an in-game action based on avoice input may be lower than a confidence score required by aserver-side process to perform the same in-game action. The processingbeing done by the customized on-device NLU model may be quicker than theprocessing done by the server-side process, but the results may be lessaccurate because of the lower threshold confidence score required totrigger an in-game action. However, by using the server-side process asconfirmation step and the customized on-device NLU model for the initialprocessing, the dual pipeline may reduce any perceived latency of usingthe server-side process to confirm the action taken is the actionintended by the user. The confidence score may be adjusted for certaincontexts. As an example and not by way of limitation, a specific voicecommand may be expected to be used frequently for a specific in-gamescenario, and the customized on-device NLU model may use the context ofthe application to assign a higher confidence score to trigger thespecific voice command. In addition to being able to execute voicecommands more quickly, the utilization of a customized on-device NLUmodel for an application may improve upon user privacy and security byfiltering out more voice inputs to determine whether the voice input isnecessary to be processed by the server-side process.

In particular embodiments, one or more client systems may receive afirst audio input comprising a first voice command of a first pluralityof voice commands associated with a first application. In particularembodiments, the one or more client systems may receive the first audioinput by one or more microphones of the one or more client systems. Inparticular embodiments, the first audio input may not comprise awake-word (i.e., the audio input does not have a wake-word). Inparticular embodiments, the first plurality of voice commands maycomprise a first set of commands executable by a customized on-devicenatural language understanding (NLU) model installed on the one or moreclient systems and a second set of commands executable by a server-sideassistant system. In particular embodiments, the customized on-deviceNLU model installed on the one or more client systems may correspond tothe first application. As an example and not by way of limitation, eachapplication may have a respective customized on-device NLU model that isgenerated by the respective application developer. In particularembodiments, the one or more client systems may receive, by the one ormore microphones of the one or more client systems, a second audio inputcomprising a second voice command of a second plurality of voicecommands associated with a second application. In particularembodiments, the second plurality of voice commands may comprise a thirdset of commands executable by a second customized on-device naturallanguage understanding (NLU) model installed on the one or more clientsystems and a fourth set of commands executable by the server-sideassistant system. Although this disclosure describes receiving an audioinput in a particular manner, this disclosure contemplates receiving anaudio input in any suitable manner.

In particular embodiments, the one or more client systems may processthe first audio input to determine the first voice command is associatedwith the first set of voice commands of the first plurality of voicecommands. In particular embodiments, the one or more client systems mayprocess the first audio input using the customized on-device NLU modelinstalled on the one or more client systems. In particular embodiments,the customized on-device NLU model may be a pattern recognition model.In particular embodiments, the one or more client systems may processthe second audio input to determine the second voice command isassociated with the third set of voice commands of the second pluralityof voice commands. In particular embodiments, the one or more clientsystems may process the second audio input using the second customizedon-device NLU model installed on the one or more client systems.Although this disclosure describes processing an audio input in aparticular manner, this disclosure contemplates processing an audioinput in any suitable manner.

In particular embodiments, the one or more client systems may execute afirst task corresponding to the first voice command responsive to thefirst audio input. In particular embodiments, the one or more clientsystems may execute the first task by the first application. Inparticular embodiments, the one or more client systems may render, byone or more display of the one or more client systems and responsive toexecuting the first task, a visual effect corresponding to the firsttask. In particular embodiments, the one or more client systems mayexecute, by the second application, a second task corresponding to thesecond voice command responsive to the second audio input. Although thisdisclosure describes executing a task in a particular manner, thisdisclosure contemplates executing a task in any suitable manner.

In particular embodiments, the one or more client systems may send thefirst audio input to the server-side assistant system. The first audioinput may be processed by an NLU model of the server-side assistantsystem to determine the first voice command is associated with the firstset of voice commands of the first plurality of voice commands. Inparticular embodiments, the one or more client systems may receive, fromthe server-side assistant system, a confirmation that the first voicecommand is associated with the first set of voice commands. Inparticular embodiments, executing the first task corresponding to thefirst voice command is further responsive to receiving the confirmation.In particular embodiments, the one or more client systems may send thesecond audio input to the server-side assistant system. In particularembodiments, the server-side assistant system may process the secondaudio input by an NLU model of the server-side assistant system todetermine the second voice command is associated with the third set ofvoice commands of the second plurality of voice commands. In particularembodiments, the one or more client systems may receive an indicationthat the second audio input is associated with the fourth set of voicecommands of the second plurality of voice commands from the server-sideassistant system. In particular embodiments, the one or more clientsystems may cancel, responsive to receiving the indication, theexecution of the second task corresponding to the second voice command.As an example and not by way of limitation, if the second applicationwas generating a visual effect of a fireball, then the secondapplication may cancel the visual effect responsive to receiving theindication. In particular embodiments, the one or more client systemsmay terminate a visual effect corresponding to the second taskresponsive to receiving the indication that the second audio input isassociated with the fourth set of voice commands of the second pluralityof voice commands. Although this disclosure describes sending an audioinput to a server-side assistant system in a particular manner, thisdisclosure describes sending an audio input to a server-side assistantsystem in any suitable manner.

In particular embodiments, the one or more client systems may determinea first confidence score for the customized on-device NLU model to matchthe first audio input to the first voice command. In particularembodiments, the first task may be executed responsive to determiningthe first confidence score exceeds the first threshold confidence score.In particular embodiments, the server-side assistant system maydetermine a second confidence score for the NLU model of the server-sideassistant system to match the second audio input to the second voicecommand using the NLU model of the server-side assistant system. Inparticular embodiments, the one or more client systems may receive anindication that the second audio input fails to match the second voicecommand responsive to the second confidence score falling below a secondthreshold confidence score. In particular embodiments, the secondthreshold confidence score is higher than the first threshold score. Inparticular embodiments, the one or more client systems may receive aconfirmation that the second audio input matches the second voicecommand responsive to the second confidence score exceeding a thresholdconfidence score.

FIG. 16 illustrates an example flow diagram of the process 1600 ofprocessing an audio input. In particular embodiments, the process 1600may include a client system 130, an assistant xbot 1602, an assistantsystem 140, a custom NLU model 1604, and an application 1606. Inparticular embodiments, there may be one or more additional componentsin the 1600 not shown. In particular embodiments, one or more componentsof the process 1600 may be combined. As an example and not by way oflimitation, the client system 130 may be combined with the assistantxbot 1602. In particular embodiments, the assistant xbot 1602 may beinstalled on the client system 130 to handle interactions with anassistant system 140. In particular embodiments, the assistant system140 may be embodied as an NLP tool. In particular embodiments, thecustom NLU 1604 may be installed on the client system 130. In particularembodiments, the application 1606 may be installed on the client system130. In particular embodiments, the application 1606 may correspond toan XR application that renders an XR environment for a user to see. Asan example and not by way of limitation, the application 1606 may renderone or more images to be displayed on one or more displays of the clientsystem 130 to portray the XR environment to the user. In particularembodiments, the application 1606 may request that one or moremicrophones of the client system 130 to be set to a listening mode. Inparticular embodiments, the client system 130 may receive an audioinput. In particular embodiments, the client system 130 may send theaudio input to the assistant xbot 1602 at step 1608. In particularembodiments, the assistant xbot 1602 may send the audio input to anassistant system 140 at step 1610 and send the audio input to the customNLU 1604 at step 1612. In particular embodiments, the assistant system140 may begin to process the audio input at step 1614. In particularembodiments, the custom NLU 1604 may begin to process the audio input atstep 1616. In particular embodiments, the custom NLU 1604 may use apattern-recognition model to quickly process the audio input. Inparticular embodiments, the custom NLU 1604 may have a lower thresholdconfidence interval than the assistant system 140 to match the audioinput to a voice command. As an example and not by way of limitation,the threshold confidence interval for the custom NLU 1604 may be +0.4and the threshold confidence interval for the assistant system 140 maybe +0.75. In particular embodiments, the custom NLU 1604 may determine amatch at step 1618. In particular embodiments, the custom NLU 1604 maysend an indication of a match to the application 1606 at step 1620. Inparticular embodiments, the application 1606 may process the indicationof a match to a voice command to perform the task corresponding to thevoice command. As an example and not by way of limitation if theapplication 1606 receives an indication that the custom NLU 1604received an audio input corresponding to a generating a fireball spellvoice command, the application 1606 may begin to render a visual effectcorresponding to the fireball spell. In particular embodiments, theapplication 1606 may send a task for the client system 130 to execute atstep 1622. The client system 130 may begin to execute the task at step1624. The assistant system 140 may subsequently determine a failed matchat step 1626. The assistant system 140 may send the indication of afailed match at step 1628. In particular embodiments, the assistantsystem 140 may determine the audio input does not match a voice commandbecause the threshold confidence interval to match an audio input to avoice command is higher than the custom NLU 1604 threshold confidenceinterval. In particular embodiments, the application 1606 may processthe indication and send a cancellation of the task to the client system130 at step 1630. In particular embodiments, the client system 130 mayprocess the cancellation request and cancel the task at step 1632. Assuch, the client system 130 may initially generate a visual effectcorresponding to executing the task at step 1624, but may change thevisual effect after receiving the cancellation request and cancellingthe task at 1632. In particular embodiments, the processing of the audioinput through using both an assistant system 140 and the custom NLU 1604may provide a check on the results of the custom NLU 1604. In that way,the results of the custom NLU 1604 may be processed quicker so that anoutput may be generated quicker if needed, but the results may beupdated if the assistant system 140 sends an indication of a failedmatch that conflicts with the output of the custom NLU 1604.

FIG. 17 illustrates an example XR environment processing an audio inputusing a customized on-device NLU model. In particular embodiments, theenvironment 1700 may be a game environment of a space shooter game. Inparticular embodiments, the environment 1700 may include an avatar 1702,a customized on-device NLU module 1704, and enemies 1706. In particularembodiments, the customized on-device NLU module 1704 may be embodiedand represented as a walkie talkie for the user to view. In particularembodiments, the visual representation of the customized on-device NLUmodule 1704 may provide an interface for the user to say voice commandsthat the application may be able to quickly process using apattern-recognition model as described herein. In particularembodiments, the application may include various voice commands that areavailable to be quickly processed by a customized on-device NLU model.The developers of the application may specify a list of voice commandsthat are available to be executed for the application. The list mayinclude subsets, such as a subset of voice commands that can be quicklyprocessed by a customized on-device NLU model installed on the clientsystem executing the application. The user may say one of the subset ofvoice commands that can be processed by the customized on-device NLUmodel by speaking to the customized on-device NLU module 1704represented as the walkie talkie. As an example and not by way oflimitation, a voice command that can be processed by the customizedon-device NLU model may be an air strike. In particular embodiments, theuser can say “call in an air strike at D20”, the customized on-deviceNLU model may process the audio input to identify the voice command andquickly send instructions to the application to perform the taskcorresponding to the voice command. The application may sendinstructions to the client system to execute the task corresponding tothe voice command. As an example and not by way of limitation, theexecution of the task may initiate the sounds of aircraft coming intothe environment 1700. In particular embodiments, an assistant system 140may also process the audio input and determine the audio input does notmatch a voice command. The assistant system 140 may send an indicationof the no match to the application. As described herein, the applicationmay update instructions and send a cancellation request to the clientsystem to cancel execution of the task. As such, continuing theairstrike example, sounds of aircraft may initially begin to play, butthen the client system may update the rendering of the task to playsounds of the aircraft leaving.

FIG. 18 illustrates an example method 1800 for processing an audioinput. A client system 130 embodied as an augmented reality headset,virtual reality headset, or extended reality headset may perform themethod 1800. The method may begin at step 1810, where one or moreclients systems may receive, by one or more microphones of the one ormore client systems, a first audio input comprising a first voicecommand of a first plurality of voice commands associated with a firstapplication. In particular embodiments, the first audio input may notcomprise a wake-word. In particular embodiments, the first plurality ofvoice commands may comprise a first set of commands executable by acustomized on-device natural-language understanding (NLU) modelinstalled on the one or more client systems and a second set of commandsexecutable by a server-side assistant system. At step 1820, the one ormore client systems may process, using the customized on-device NLUmodel installed on the one or more client systems, the first audio inputto determine the first voice command is associated with the first set ofvoice commands of the first plurality of voice commands. At step 1830,the one or more client systems may execute, by the first application, afirst task corresponding to the first voice command responsive to thefirst audio input. Particular embodiments may repeat one or more stepsof the method of FIG. 18 , where appropriate. Although this disclosuredescribes and illustrates particular steps of the method of FIG. 18 asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method of FIG. 18 occurring in any suitable order.Moreover, although this disclosure describes and illustrates an examplemethod for processing an audio input including the particular steps ofthe method of FIG. 18 , this disclosure contemplates any suitable methodfor processing an audio input including any suitable steps, which mayinclude all, some, or none of the steps of the method of FIG. 18 , whereappropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 18 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 18 .

Tunable Confidence Intervals for On-Device NLU Models

In particular embodiments, a client system may implement voice commandswithin an XR environment via a voice SDK, which allows XR applicationsto easily integrate voice commands on XR devices (e.g., the clientsystem). In particular embodiments, one or more computing systems (e.g.,a server-side assistant system) may customize the tolerance intervalsused by natural-language understanding (NLU) models for processing voiceinteractions via the voice SDK. Developers may have issues initiallyadding voice interactions to their applications. To reduce thecomplexity and improve upon the developer experience, the voice SDK mayinclude voice interactions that allow users to maintain immersion,enable multitasking, and make content more accessible. As an example andnot by way of limitation, the voice SDK may provide interactions thatallow voice-driven gameplay, such as using voice commands to navigate anXR environment. As another example and not by way of limitation, thevoice SDK may provide interactions that allow voice navigation andsearch, such as a voice command to quickly switch a XR environment toanother context (e.g., from one XR generated environment to a new XRgenerated environment) for an application. As another example and not byway of limitation, the voice SDK may provide discovery and usereducation, such as providing an interface to quickly navigate possiblevoice commands available to the user within the application. Voiceinterfaces can unlock entirely new ways of interacting with an XRenvironment. The voice SDK gives developers a way to integrate AI-drivenvoice experiences into XR applications.

In particular embodiments, the voice SDK may include functionality thatis implemented client-side, such that a client device may include one ormore components generated by the voice SDK installed and operating onthe device. The voice SDK may be powered by a NLP tool, which may run anatural-language processing (NLP) service. Using the NLP tool,developers may input a small sample of training utterances (e.g., from asingle utterance to a few utterances) for a given interaction, and theNLP tool can expand these input utterances out to hundreds of userutterances for the given interaction. The expansion of input utterancesout to hundreds of user utterances may allow the system (e.g., clientsystem or server-side assistant system) to handle variances in voiceinputs. As an example and not by way of limitation, if an inpututterance included “move the box” the input utterance can be expandedout to include “move the object”, “move the item”, and the like. Byexpanding out the input utterances, the system may be able to stillhandle a voice command the user provides even if it isn't exactly thesame initial input utterance specified by the developer. The developermay specify a threshold level of match that needs to be performed forcertain user utterances. As an example, the developer may specify thatthe user utterance needs to match the input utterance with a 0.95confidence, then the user may be required to say the exact inpututterance to perform a voice command.

In particular embodiments, the voice SDK may include a pre-constructedset of over 50 default interactions that application developers canessentially plug-and-play as voice interactions in their application.The pre-constructed set of default interactions may continually bemodified (e.g., the set may be added to and changed to remove previousdefault interactions). Some of the default interactions may include oneor more of go_back, go_forward, play/pause/resume, select_item, cancel,confirmation, share, open_resource, increase_volume, decrease_volume,date/time, create_timer, create_playlist, like_music, duration,get_time, create_alarm, and the like. By providing a large set ofdefault interactions, developers may be enabled to use the voice SDK outof the box without further modification. Providing the defaultinteractions may reduce the barrier to implement the voice SDKcomponents into applications. The voice SDK also includes guidelines forhow to handle the attention system, microphone states(listening/recording/off), and errors. For other interactions thatdevelopers would like to specify, the developers may set up newinteractions as follows: (1) set up an application by adding a voiceservice component to an application, where components are built withsimplicity in mind, built for designers and programmers alike; (2) addutterances to the NLP tool, by telling the application what you want itto respond to by entering simple phrases such as “Make the cube blue”;(3) trigger component callbacks, where as long as components in anapplication have public methods, the user can trigger them via eventstied to voice commands using an understanding viewer.

In particular embodiments, the voice SDK may allow a developer toimplement a customized on-device NLU model and a general NLU model forthe developer's application. The guidelines for implementing anon-device NLU model. Developers may use the voice SDK to customizeconfidence intervals for matching a voice utterance to an in-applicationinteraction when setting up the NLU models. The confidence interval mayspecify a range of confidence values that may be accepted for the NLUmodel to match a voice utterance to a particular voice command. For somevoice commands, a higher threshold may be set for the confidenceintervals for the voice command, which may make the voice command moredifficult to implement for the user. As an example and not by way oflimitation, for the final challenge of a game, the user may have to casta complex spell. For this particular interaction, the developer can seta higher allowable confidence interval (e.g., 0.9+ confidence needed tomatch), so the spell fails if the user makes minor mispronunciations orerrors, thus making the casting of this final spell more of a challenge(and more satisfying when they finally cast it correctly). In particularembodiments, for some voice commands, a lower threshold may be set forthe confidence intervals, which may make the voice command easier toimplement for the user. As an example and not by way of limitation, whenthe user approaches a door, perhaps the user needs to say “open sesame”to trigger an [open_door] interaction in the game. However, users mayoften mispronounce this and instead say, for example, “open says-a-me.”With the normal confidence intervals (e.g., 0.7+), the system wouldlikely not match this mispronunciation to the [open_door] interaction.Thus, for this particular interaction, the developer may set a lowerallowable confidence interval (e.g., 0.4+), such that minor variationsand mispronunciations might still be considered matches by the NLUmodel, allowing the desired in-game action to actually happen. Theseconfidence intervals can be tuned for both the client-side NLU modelsand for server-side NLP tool processing. In particular embodiments,these confidence intervals can also be applied for controlling when toturn on the microphone (i.e., for the no-wake-word use case above). Thetuning of these confidence intervals primarily applies to matching ofintents/slots by NLU models (either client- or server-side). Inparticular embodiments, the confidence intervals of ASR models may alsobe tuned. Similarly, the concept of tuning confidence intervals can beextended out to one-shot language models (which essentially combine ASRand NLU into a single model) used for customizable on-device models.With these models, the audio input is basically pattern-matched by anon-device NLU model. The confidence needed for the on-device NLU modelto indicate a match could be tuned up or down as needed by theapplication developer.

In particular embodiments, there may be several scenarios where thedevelopers may want to customize the confidence level/interval neededfor the NLU model to match intents/slots. The several scenarios mayinclude phonetic adjacency, easy vs. challenging tasks, avoiding falsewakes/actions, stressful/critical scenarios, non-native speakers,context-based scenarios, and the like. In particular embodiments, forphonetic adjacency, at a high-level, where two or more words accepted bythe application (either as intents or slots) are phonetically similar insound, the developer may want to increase the confidence level needed toindicate a match. As an example and not by way of limitation, forminimal pairs, where pairs of words sound very similar (e.g., bat, pat,rat, vat, etc.), the developer would want to set a higher confidenceinterval before allowing the model to indicate a match. As anotherexample and not by way of limitation, the more possible words recognizedby the model as being similar, the higher the developer would want theconfidence level to be prior to a match. So start with a defaultconfidence for distinct commands, and then as the commands get clusteredtogether, start tuning them up. Similarly, for an application wherecommands are distinct words, can use a default/lower confidence. Foreasy vs. challenging tasks, developers may desire to turn up (requirehigher confidence) for tasks (e.g., a voice command) that the developerswant to be harder for the user or for a particular code word/phrase thatthe developers want the user to speak exactly. In particularembodiments, for standard/easy tasks, the developers could tune thevoice command to a default/lower confidence level. In particularembodiments, for avoiding false wakes/actions, in some scenarios,developers may want to make sure the system isn't misclassifying anintent/slot because a false execution would lead to a negativeexperience (e.g., ordering the wrong food, buying the wrong stock). Todo so, the developers may raise the confidence level for the NLU modelto match intents/slots (e.g., ordering food). Similarly, where falseaccepts are less problematic, the developer could tune down theconfidence level required for the NLU model to match intents/slots. Inparticular embodiments, for stressful/critical scenarios, developers maywant to ensure that voice commands are recognized and executed, even ifthe user is incorrect in pronunciation. That is, the user may be under ahigh-stress scenario and incorrectly pronouncing a voice command. Toaccount for the effects of a high-stress scenario, the developers maytune down the confidence level for the NLU model to match intents/slots.In particular embodiments, for non-native speakers, the developers maywant to tune down the confidence intervals on the NLU model to allow forvariations and mispronunciations due to accents. To enable the featureof adjusting for non-native speakers, the system may analyze the voiceinputs of the user to determine whether to apply a non-native speakeradjustment to the confidence intervals on the NLU model. In particularembodiments, the developers may tune confidence levels of the NLU modelfor certain scenarios based on context. In some scenarios, the systemmight be expecting certain commands. As an example and not by way oflimitation, if the user is standing in front of a door, the system maybe expecting the user to say “open the door”, then can dynamically tunedown the confidence interval for this voice command. Similarly, if theuser is far away from the door and looking away, the system coulddynamically tune up the confidence interval for the same command (so asto not accidentally open the door). In particular embodiments, the voiceSDK may enable applications to activate the microphone without using awake word or using a manual input. As an example and not by way oflimitation, the microphone may be automatically put into a listeningmode responsive to in-application events occurring. For example, whenthe user gazes at a virtual door in a VR game and/or gets close to thedoor, this can be an event that triggers the microphone to turn on toallow the user to input voice commands with respect to the door.

In particular embodiments, the voice SDK may enable a developer toprovide user education in immersive ways for the user. The usereducation may be different depending on the user. As an example and notby way of limitation, the user education provided to a returning uservs. a new user may be different. Different voice commands may beprovided to different users (e.g., based on level of a user within agame application, based on features a user may have paid for within anapplication). As an example and not by way of limitation, a user may beplaying a fantasy VR game and open a spellbook and begin flippingthrough the pages. The user may find a spell to cast, and begin speakingin Elvish. The user can say “Yal-Sanda”, as a shield materializes aroundthe user. The user may turn to an NPC and ask, “What else can I say?”.The NPC may respond with instructions for how to cast a couple morespells the user has not done yet.

In particular embodiments, the voice SDK may provide voice-enabledmultitasking to various applications. In particular embodiments, fortraditional games multitasking while playing a game can be difficultwith a standard controller interface. A user may be able to performmultiple tasks using voice commands. As an example and not by way oflimitation, in a real-time military strategy game, a user can buildthings in their base while supervising a firefight elsewhere on a map.As another example and not by way of limitation, in a fantasyrole-playing game, the user can instruct an NPC to fetch an item from astore while the user is smithing an axe. As yet another example and notby way of limitation, in an virtual exploration game, the user may queryan environment while interacting with an object.

In particular embodiments, the voice SDK may provide voice-enabledsearch. Typing and searching in XR may be awkward because of the use ofcontrollers to type in an input. Voice may be a natural input method forsearching, either through dictation, or by speaking naturally in searchof something specific. The application may be able to provide a resultquickly and seamlessly when a user knows exactly what to look for. Theuse of a voice input eliminates the need to tediously raycastletter-by-letter when searching for something within a VR application.

In particular embodiments, one or more computing systems may be embodiedas a server-side assistant system, client system, and the like. Inparticular embodiments, the one or more computing systems may receive anaudio input of a user from a XR display device. In particularembodiments, the audio input may comprise a first voice command of aplurality of voice commands associated with a first application, whereinthe plurality of voice commands are executable by a NLU model. Inparticular embodiments, the plurality of voice commands comprise one ormore of avatar movement, avatar actions, object movement, objectactions, application settings, or system settings. Although thisdisclosure describes receiving an audio input in a particular manner,this disclosure contemplates receiving an audio input in any suitablemanner.

In particular embodiments, the one or more computing systems maydetermine a first context of the user with respect to an XR environment.As an example and not by way of limitation, the one or more computingsystems may determine the context of the user, such as whether the userhas previously interacted with the XR environment. As another exampleand not by way of limitation, the one or more computing systems maydetermine application context the user is in. For instance, the user maybe in a hard level of a game application. Although this disclosuredescribes determining a context of a user in a particular manner, thisdisclosure contemplates determining a context of a user in any suitablemanner.

In particular embodiments, the one or more computing systems maydetermine a tunable confidence interval for the NLU model to match theaudio input to the first voice command based on the first context. Inparticular embodiments, the tunable confidence interval varies based onthe user context. In particular embodiments the tunable confidenceinterval is set to a first tunable confidence level based on the firstcontext of the user. In particular embodiments, adjusting the tunableconfidence interval to a second tunable confidence level based oncontent of the first application. In particular embodiments, the secondtunable confidence level is higher than the first tunable confidencelevel. In particular embodiments, the content is a difficult game leveland the second tunable confidence level is higher to promote the user toprecisely state voice commands. In particular embodiments, determiningthe audio input matches the first voice command is further based on thesecond tunable confidence level. In particular embodiments, the secondtunable confidence level is different from the first tunable confidencelevel. In particular embodiments, the one or more computing systems maydetermine whether the audio input matches the first voice command basedon the second tunable confidence level by a customizable NLU model ofthe XR display device. In particular embodiments, the one or morecomputing systems may provide instructions to the XR display device toplace one or more microphones of the XR display device into a listeningmode responsive to determining the first unable confidence level exceedsa threshold tunable confidence level. As an example and not by way oflimitation, the XR display device may set one or more microphones into alistening mode when the first tunable confidence level exceeds athreshold tunable confidence level. Although this disclosure describesdetermining a tunable confidence interval in a particular manner, thisdisclosure contemplates determining a tunable confidence interval in anysuitable manner.

In particular embodiments, the one or more computing systems maydetermine whether the audio input matches the first voice command basedon the first tunable confidence level by the NLU model. In particularembodiments, the one or more computing systems may determine audio inputcomprises one or more words that are phonetically adjacent to one ormore other words. In particular embodiments, the one or more computingsystems may adjust the tunable confidence interval to a second tunableconfidence level responsive to determining the audio input comprises oneor more words that are phonetically adjacent to one or more other words.In particular embodiments, the second tunable confidence level is higherthan the first tunable confidence level. In particular embodiments,determining the audio input matches the first voice command is furtherbased on the second tunable confidence level. In particular embodiments,the one or more computing systems may determine a difficult associatedwith a first task. In particular embodiments, the one or more computingsystems may adjust the tunable confidence interval to a second tunableconfidence level based on the difficulty associated with the first task.In particular embodiments, the one or more computing systems may accessa list of predetermined tasks. In particular embodiments, the one ormore computing systems may adjust the tunable confidence interval to asecond tunable confidence level based on the first task matching onetask out of the list of predetermined tasks. In particular embodiments,the one or more computing systems may receive an indication that thefirst user has a speech characteristic. In particular embodiments, theone or more computing systems may adjust the tunable confidence intervalto a second tunable confidence level based on the speech characteristic.Although this disclosure describes determining whether the audio inputmatches a first voice command in a particular manner, this disclosurecontemplates determining whether the audio input matches a first voicecommand in any suitable manner.

In particular embodiments, the one or more computing systems maydetermine whether the audio input matches the first voice command byidentifying one or more intents or one or more slots associated with theaudio input. In particular embodiments, the one or more computingsystems may access one or more first voice command intents or one ormore first voice command slots associated with the first voice command.In particular embodiments, the one or more computing systems maydetermine whether the identified one or more intents or the one or moreslots match the one or more first voice command intents or the one ormore first voice command slots based on the first tunable confidencelevel. Although this disclosure describes determining whether an audioinput matches a voice command in a particular manner, this disclosurecontemplates determining whether an audio input matches a voice commandin any suitable manner.

FIG. 19 illustrates an example flow diagram for a process 1900 of tuninga confidence interval. In particular embodiments, one or more computingsystems may perform the process 1900. As an example and not by way oflimitation, a server-side assistant system may perform the process 1900.In particular embodiments, a client system may perform the process 1900as described herein. As an example and not by way of limitation, an XRdisplay device may perform the process 1900. In particular embodiments,the one or more computing systems may receive an audio input 1902. Inparticular embodiments, the one or more computing systems may determinea first tunable confidence interval 1904 to meet to determine an audioinput matches a voice command. The first tunable confidence interval1904 may be a default tunable confidence interval. In particularembodiments, the tunable confidence interval may be set between 0.01to 1. Although other ranges may be used for the tunable confidenceinterval. In particular embodiments, the one or more computing systemsmay send the first tunable confidence interval 1904 to a tunableconfidence adjustment 1906. In particular embodiments, the tunableconfidence adjustment 1906 may include a plurality of parameters toadjust the first tunable confidence interval 1904. In particularembodiments, the parameters may include phonetic adjacency 1908, easyvs. challenging tasks 1910, false wakes/actions 1912, stressful/criticalscenarios 1914, non-native speakers 1916, and context-based scenarios1918. In particular embodiments, phonetic adjacency 1908 determineswhether the audio input 1902 contains one or more words that sound likeother words. In particular embodiments, the tunable confidenceadjustment 1906 may increase the tunable confidence interval to requirea high confidence interval to match the audio input 1902 to a voicecommand if the audio input 1902 is determined that one or more words isphonetically adjacent to other words. In particular embodiments, theeasy vs. challenging tasks 1910 may determine whether an applicationdeveloper has previously modified the tunable confidence interval forspecific voice commands. As an example and not by way of limitation, ifthe voice command to open a door in a game application is intended to beeasy, then the tunable confidence interval for the command may be setlow to increase the likelihood of processing the voice command. Inparticular embodiments, the false wakes/actions 1912 may specify a listof voice commands that an application developer would not want toaccidentally occur. As an example and not by way of limitation, placingan order for food may be identified as a voice command that a user wouldnot want to have a false action. As such, the tunable confidenceinterval to match an audio input to a placing an order for food voicecommand may be set high. In particular embodiments, stressful/criticalscenarios 1914 may specify certain scenarios that may be stressful orcritical in an application. As an example and not by way of limitation,in a war field environment, the user may be more stressed than normal.As such, the user may mess up saying voice commands. To account for theeffects stress would have on pronunciation of voice commands, theapplication may identify certain situations or scenarios that the usermay be stressed and all tunable confidence intervals may be tuned up torespond better to attempted voice commands. In particular embodiments,non-native speakers 1916 may specify a language of an application anddetermine when non-native speakers of that language is attempting to saya voice command. The tunable confidence interval may be tuned down to bemore accepting of variations of pronunciations of a voice command from anon-native speaker. In particular embodiments, the context-basedscenarios 1918 may specify certain scenarios where the tunableconfidence intervals may be adjusted. As an example and not by way oflimitation, for a tutorial section of an application, the tunableconfidence intervals of voice commands may be tuned up so that theapplication may respond to voice commands better (e.g., process audioinputs as voice commands).

As an example and not by way of limitation, for a boss level, thedifficulty of the level may include correctly pronouncing voice commandsso the tunable confidence intervals for voice commands may be tuned downto may the difficult of performing voice commands harder. For instance,to cast a spell using a voice command, the user has to be more precisein their pronunciation. In particular embodiments, the tunableconfidence adjustment 1906 may consider one or more parameters inadjusting the tunable confidence interval to generate a second tunableconfidence interval 1920. The tunable confidence interval 1920 may besent to the NLU model 1922 to determine whether the audio input matchesa voice command. The NLU model 1922 may subsequently identify theintents and slots associated with the audio input after identifying avoice command associated with the audio input. The intents and slots maybe sent to an application to process and generate a response to bepresented by a client system.

FIG. 20 illustrates an example method 2000 for turning a confidenceinterval. In particular embodiments, one or more computing systemsembodied as server-side assistant system may perform the method oftuning a confidence interval. In particular embodiments, a client systemmay also perform the method of tuning a confidence interval. The methodmay begin at step 2010, where one or more computing systems may receive,from an extended reality (XR) display device, an audio input of a userof the XR display device. In particular embodiments, the audio inputcomprises a first voice command of a plurality of voice commandsassociated with a first application. In particular embodiments, theplurality of voice commands are executable by a natural-languageunderstanding (NLU) model. At step 2020, the one or more computingsystems may determine a first context of the user with respect to anextended reality (XR) environment. At step 2030, the one or morecomputing systems may determine, based on the first context, a tunableconfidence interval for the NLU model to match the audio input to thefirst voice command. In particular embodiments, the tunable confidenceinterval may vary based on user context. In particular embodiments thetunable confidence interval may be set to a first tunable confidencelevel based on the first context of the user. At step 2040, the one ormore computing systems may determine, by the NLU model, whether theaudio input matches the first voice command based on the first tunableconfidence level. At step 2050, the one or more computing systems mayexecute, by the first application, a first task corresponding to thefirst voice command responsive to determining the audio input matchesthe first voice command. Particular embodiments may repeat one or moresteps of the method of FIG. 20 , where appropriate. Although thisdisclosure describes and illustrates particular steps of the method ofFIG. 20 as occurring in a particular order, this disclosure contemplatesany suitable steps of the method of FIG. 20 occurring in any suitableorder. Moreover, although this disclosure describes and illustrates anexample method for tuning a confidence interval including the particularsteps of the method of FIG. 20 , this disclosure contemplates anysuitable method for tuning a confidence interval including any suitablesteps, which may include all, some, or none of the steps of the methodof FIG. 20 , where appropriate. Furthermore, although this disclosuredescribes and illustrates particular components, devices, or systemscarrying out particular steps of the method of FIG. 20 , this disclosurecontemplates any suitable combination of any suitable components,devices, or systems carrying out any suitable steps of the method ofFIG. 20 .

Social Graphs

FIG. 21 illustrates an example social graph 2100. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 2100 in one or more data stores. In particularembodiments, the social graph 2100 may include multiple nodes—which mayinclude multiple user nodes 2102 or multiple concept nodes 2104—andmultiple edges 2106 connecting the nodes. Each node may be associatedwith a unique entity (i.e., user or concept), each of which may have aunique identifier (ID), such as a unique number or username. The examplesocial graph 2100 illustrated in FIG. 21 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, a client system 130, anassistant system 140, or a third-party system 170 may access the socialgraph 2100 and related social-graph information for suitableapplications. The nodes and edges of the social graph 2100 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 2100.

In particular embodiments, a user node 2102 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 2102 correspondingto the user, and store the user node 2102 in one or more data stores.Users and user nodes 2102 described herein may, where appropriate, referto registered users and user nodes 2102 associated with registeredusers. In addition or as an alternative, users and user nodes 2102described herein may, where appropriate, refer to users that have notregistered with the social-networking system 160. In particularembodiments, a user node 2102 may be associated with informationprovided by a user or information gathered by various systems, includingthe social-networking system 160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 2102 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 2102 maycorrespond to one or more web interfaces.

In particular embodiments, a concept node 2104 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node2104 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 2104 may be associated with one or more dataobjects corresponding to information associated with concept node 2104.In particular embodiments, a concept node 2104 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 2100 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 2104. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 2102 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 2104 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 2104.

In particular embodiments, a concept node 2104 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject representing an action or activity. As an example and not by wayof limitation, a third-party web interface may include a selectable iconsuch as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 2102 corresponding to the user and a conceptnode 2104 corresponding to the third-party web interface or resource andstore edge 2106 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 2100 maybe connected to each other by one or more edges 2106. An edge 2106connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 2106 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 2106 connecting the first user's user node 2102 to thesecond user's user node 2102 in the social graph 2100 and store edge2106 as social-graph information in one or more of data stores 164. Inthe example of FIG. 21 , the social graph 2100 includes an edge 2106indicating a friend relation between user nodes 2102 of user “A” anduser “B” and an edge indicating a friend relation between user nodes2102 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 2106 with particular attributes connectingparticular user nodes 2102, this disclosure contemplates any suitableedges 2106 with any suitable attributes connecting user nodes 2102. Asan example and not by way of limitation, an edge 2106 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in the social graph 2100 byone or more edges 2106. The degree of separation between two objectsrepresented by two nodes, respectively, is a count of edges in ashortest path connecting the two nodes in the social graph 2100. As anexample and not by way of limitation, in the social graph 2100, the usernode 2102 of user “C” is connected to the user node 2102 of user “A” viamultiple paths including, for example, a first path directly passingthrough the user node 2102 of user “B,” a second path passing throughthe concept node 2104 of company “CompanyName” and the user node 2102 ofuser “D,” and a third path passing through the user nodes 2102 andconcept nodes 2104 representing school “SchoolName,” user “G,” company“CompanyName,” and user “D.” User “C” and user “A” have a degree ofseparation of two because the shortest path connecting theircorresponding nodes (i.e., the first path) includes two edges 2106.

In particular embodiments, an edge 2106 between a user node 2102 and aconcept node 2104 may represent a particular action or activityperformed by a user associated with user node 2102 toward a conceptassociated with a concept node 2104. As an example and not by way oflimitation, as illustrated in FIG. 21 , a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “read” a concept, eachof which may correspond to an edge type or subtype. A concept-profileinterface corresponding to a concept node 2104 may include, for example,a selectable “check in” icon (such as, for example, a clickable “checkin” icon) or a selectable “add to favorites” icon. Similarly, after auser clicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“SongName”) using a particular application (a third-party online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 2106 and a “used” edge (as illustrated in FIG. 21 )between user nodes 2102 corresponding to the user and concept nodes 2104corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 2106 (asillustrated in FIG. 21 ) between concept nodes 2104 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 2106corresponds to an action performed by an external application (thethird-party online music application) on an external audio file (thesong “SongName”). Although this disclosure describes particular edges2106 with particular attributes connecting user nodes 2102 and conceptnodes 2104, this disclosure contemplates any suitable edges 2106 withany suitable attributes connecting user nodes 2102 and concept nodes2104. Moreover, although this disclosure describes edges between a usernode 2102 and a concept node 2104 representing a single relationship,this disclosure contemplates edges between a user node 2102 and aconcept node 2104 representing one or more relationships. As an exampleand not by way of limitation, an edge 2106 may represent both that auser likes and has used at a particular concept. Alternatively, anotheredge 2106 may represent each type of relationship (or multiples of asingle relationship) between a user node 2102 and a concept node 2104(as illustrated in FIG. 21 between user node 2102 for user “E” andconcept node 2104 for “online music application”).

In particular embodiments, the social-networking system 160 may createan edge 2106 between a user node 2102 and a concept node 2104 in thesocial graph 2100. As an example and not by way of limitation, a userviewing a concept-profile interface (such as, for example, by using aweb browser or a special-purpose application hosted by the user's clientsystem 130) may indicate that he or she likes the concept represented bythe concept node 2104 by clicking or selecting a “Like” icon, which maycause the user's client system 130 to send to the social-networkingsystem 160 a message indicating the user's liking of the conceptassociated with the concept-profile interface. In response to themessage, the social-networking system 160 may create an edge 2106between user node 2102 associated with the user and concept node 2104,as illustrated by “like” edge 2106 between the user and concept node2104. In particular embodiments, the social-networking system 160 maystore an edge 2106 in one or more data stores. In particularembodiments, an edge 2106 may be automatically formed by thesocial-networking system 160 in response to a particular user action. Asan example and not by way of limitation, if a first user uploads apicture, reads a book, watches a movie, or listens to a song, an edge2106 may be formed between user node 2102 corresponding to the firstuser and concept nodes 2104 corresponding to those concepts. Althoughthis disclosure describes forming particular edges 2106 in particularmanners, this disclosure contemplates forming any suitable edges 2106 inany suitable manner.

Vector Spaces and Embeddings

FIG. 22 illustrates an example view of a vector space 2200. Inparticular embodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 2200 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 2200 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 2200 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 2200(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 2210, 2220, and 2230 may be represented as pointsin the vector space 2200, as illustrated in FIG. 22 . An n-gram may bemapped to a respective vector representation. As an example and not byway of limitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 2200, respectively, by applying a function

defined by a dictionary, such that

={right arrow over (π)}(t₁) and

={right arrow over (π)}(t₂). As another example and not by way oflimitation, a dictionary trained to map text to a vector representationmay be utilized, or such a dictionary may be itself generated viatraining. As another example and not by way of limitation, aword-embeddings model may be used to map an n-gram to a vectorrepresentation in the vector space 2200. In particular embodiments, ann-gram may be mapped to a vector representation in the vector space 2200by using a machine leaning model (e.g., a neural network). The machinelearning model may have been trained using a sequence of training data(e.g., a corpus of objects each comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 2200 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 2200, respectively, by applying a function {rightarrow over (π)}, such that

={right arrow over (π)}(e₁) and

={right arrow over (π)}(e₂). In particular embodiments, an object may bemapped to a vector based on one or more properties, attributes, orfeatures of the object, relationships of the object with other objects,or any other suitable information associated with the object. As anexample and not by way of limitation, a function {right arrow over (π)}may map objects to vectors by feature extraction, which may start froman initial set of measured data and build derived values (e.g.,features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 2200. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{\rightharpoonup}{v_{1}} \cdot \overset{\rightharpoonup}{v_{2}}}{{\overset{\rightharpoonup}{v_{1}}}{\overset{\rightharpoonup}{v_{2}}}}.$

As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 2200. As an example and not by way of limitation,vector 2210 and vector 2220 may correspond to objects that are moresimilar to one another than the objects corresponding to vector 2210 andvector 2230, based on the distance between the respective vectors.Although this disclosure describes calculating a similarity metricbetween vectors in a particular manner, this disclosure contemplatescalculating a similarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 23 illustrates an example artificial neural network (“ANN”) 2300.In particular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 2300 may comprise an inputlayer 2310, hidden layers 2320, 2330, 2340, and an output layer 2350.Each layer of the ANN 2300 may comprise one or more nodes, such as anode 2305 or a node 2315. In particular embodiments, each node of an ANNmay be connected to another node of the ANN. As an example and not byway of limitation, each node of the input layer 2310 may be connected toone of more nodes of the hidden layer 2320. In particular embodiments,one or more nodes may be a bias node (e.g., a node in a layer that isnot connected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 23 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 23 depicts a connection between each node of the inputlayer 2310 and each node of the hidden layer 2320, one or more nodes ofthe input layer 2310 may not be connected to one or more nodes of thehidden layer 2320.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 2320 may comprise the output of one or morenodes of the input layer 2310. As another example and not by way oflimitation, the input to each node of the output layer 2350 may comprisethe output of one or more nodes of the hidden layer 2340. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}\left( s_{k} \right)} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}\left( s_{k} \right)} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k)(s_(k))=max(0, s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection2325 between the node 2305 and the node 2315 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 2305 is used as an input to the node 2315. As anotherexample and not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 2300 and an expected output. As another example andnot by way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular photo may have a privacy setting specifying that the photomay be accessed only by users tagged in the photo and friends of theusers tagged in the photo. In particular embodiments, privacy settingsmay allow users to opt in to or opt out of having their content,information, or actions stored/logged by the social-networking system160 or assistant system 140 or shared with other systems (e.g., athird-party system 170). Although this disclosure describes usingparticular privacy settings in a particular manner, this disclosurecontemplates using any suitable privacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 2100. A privacy setting may bespecified for one or more edges 2106 or edge-types of the social graph2100, or with respect to one or more nodes 2102, 2104 or node-types ofthe social graph 2100. The privacy settings applied to a particular edge2106 connecting two nodes may control whether the relationship betweenthe two entities corresponding to the nodes is visible to other users ofthe online social network. Similarly, the privacy settings applied to aparticular node may control whether the user or concept corresponding tothe node is visible to other users of the online social network. As anexample and not by way of limitation, a first user may share an objectto the social-networking system 160. The object may be associated with aconcept node 2104 connected to a user node 2102 of the first user by anedge 2106. The first user may specify privacy settings that apply to aparticular edge 2106 connecting to the concept node 2104 of the object,or may specify privacy settings that apply to all edges 2106 connectingto the concept node 2104. As another example and not by way oflimitation, the first user may share a set of objects of a particularobject-type (e.g., a set of images). The first user may specify privacysettings with respect to all objects associated with the first user ofthat particular object-type as having a particular privacy setting(e.g., specifying that all images posted by the first user are visibleonly to friends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client system130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160.

Systems and Methods

FIG. 24 illustrates an example computer system 2400. In particularembodiments, one or more computer systems 2400 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 2400 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 2400 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 2400.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems2400. This disclosure contemplates computer system 2400 taking anysuitable physical form. As example and not by way of limitation,computer system 2400 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 2400 may include one or more computersystems 2400; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 2400 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 2400 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 2400 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 2400 includes a processor2402, memory 2404, storage 2406, an input/output (I/O) interface 2408, acommunication interface 2410, and a bus 2412. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 2402 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 2402 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 2404, or storage 2406; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 2404, or storage 2406. In particularembodiments, processor 2402 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor2402 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor2402 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 2404 or storage 2406, and the instruction caches may speed upretrieval of those instructions by processor 2402. Data in the datacaches may be copies of data in memory 2404 or storage 2406 forinstructions executing at processor 2402 to operate on; the results ofprevious instructions executed at processor 2402 for access bysubsequent instructions executing at processor 2402 or for writing tomemory 2404 or storage 2406; or other suitable data. The data caches mayspeed up read or write operations by processor 2402. The TLBs may speedup virtual-address translation for processor 2402. In particularembodiments, processor 2402 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 2402 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 2402 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 2402. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 2404 includes main memory for storinginstructions for processor 2402 to execute or data for processor 2402 tooperate on. As an example and not by way of limitation, computer system2400 may load instructions from storage 2406 or another source (such as,for example, another computer system 2400) to memory 2404. Processor2402 may then load the instructions from memory 2404 to an internalregister or internal cache. To execute the instructions, processor 2402may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 2402 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor2402 may then write one or more of those results to memory 2404. Inparticular embodiments, processor 2402 executes only instructions in oneor more internal registers or internal caches or in memory 2404 (asopposed to storage 2406 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 2404 (asopposed to storage 2406 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor2402 to memory 2404. Bus 2412 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 2402 and memory 2404and facilitate accesses to memory 2404 requested by processor 2402. Inparticular embodiments, memory 2404 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 2404 may include one ormore memories 2404, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 2406 includes mass storage for dataor instructions. As an example and not by way of limitation, storage2406 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 2406 may include removable or non-removable (or fixed)media, where appropriate. Storage 2406 may be internal or external tocomputer system 2400, where appropriate. In particular embodiments,storage 2406 is non-volatile, solid-state memory. In particularembodiments, storage 2406 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 2406taking any suitable physical form. Storage 2406 may include one or morestorage control units facilitating communication between processor 2402and storage 2406, where appropriate. Where appropriate, storage 2406 mayinclude one or more storages 2406. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 2408 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 2400 and one or more I/O devices. Computersystem 2400 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 2400. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 2408 for them. Where appropriate, I/Ointerface 2408 may include one or more device or software driversenabling processor 2402 to drive one or more of these I/O devices. I/Ointerface 2408 may include one or more I/O interfaces 2408, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 2410 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 2400 and one or more other computer systems 2400 or oneor more networks. As an example and not by way of limitation,communication interface 2410 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 2410 for it. As an example and not by way oflimitation, computer system 2400 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 2400 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 2400 may include any suitable communicationinterface 2410 for any of these networks, where appropriate.Communication interface 2410 may include one or more communicationinterfaces 2410, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 2412 includes hardware, software, or bothcoupling components of computer system 2400 to each other. As an exampleand not by way of limitation, bus 2412 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 2412may include one or more buses 2412, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by an extended reality (XR)display device: receiving, by the XR display device, a gesture-basedinput from a first user of the XR display device; processing, using agesture-detection model, the gesture-based input to identify a firstgesture; receiving, by the XR display device, an audio input from thefirst user, wherein the audio input comprises a first voice command;processing, using a natural-language model, the audio input to identifyone or more intents or one or more slots associated with the first voicecommand; determining whether the identified first gesture matches thefirst voice command; and executing, responsive to the identified firstgesture matching the first voice command and by the XR display device, afirst task corresponding to the first voice command based on theidentified first gesture and the identified one or more intents or oneor more slots.
 2. The method of claim 1, further comprising: receiving atertiary input, wherein the tertiary input comprises one or more of atouch input, gaze input, or pose input; and determining whether thetertiary input matches the first voice command, wherein executing thefirst task is further based on the tertiary input.
 3. The method ofclaim 2, wherein executing the first task is further based on an orderof receiving the gesture-based input, the audio input, and the tertiaryinput.
 4. The method of claim 1, further comprising: placing one or moremicrophones of the XR display device into a listening mode responsive toidentifying the first gesture.
 5. The method of claim 1, wherein theprocessing of the audio input is responsive to both identifying thefirst gesture and receiving the audio input from the first user.
 6. Themethod of claim 1, further comprising: rendering, for one or moredisplays of the XR display device, a visual feedback responsive toexecuting the first task.
 7. The method of claim 1, further comprising:rendering, for one or more displays of the XR display device, a userinterface comprising a menu of one or more activatable user interfaceelements responsive to executing the first task.
 8. The method of claim1, further comprising: rendering, for one or more displays of the XRdisplay device, information corresponding to frequently asked questionsresponsive to executing the first task.
 9. The method of claim 1,further comprising: capturing, by one or more cameras of the XR displaydevice, one or more images corresponding to a real-world environment ofthe first user; and processing, using a machine-learning model, the oneor more images to identify one or more real-world objects within the oneor more images.
 10. The method of claim 9, further comprising: analyzingone or more of the identified real-world objects to perform the firsttask.
 11. The method of claim 10, wherein the first task comprises:responsive to analyzing one or more of the identified real-worldobjects, identifying text corresponding to the one or more real-worldobjects, wherein the text is in a first language; translating the textfrom the first language to a second language; and presenting, by the XRdisplay device, the translated text to the first user.
 12. The method ofclaim 10, wherein the first task comprises: responsive to analyzing oneor more of the identified real-world objects, identifying one or moreonline media corresponding to the one or more real-world objects; andpresenting, by the XR display device, content corresponding to theidentified online media to the first user.
 13. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to: receive, by a XR display device, agesture-based input from a first user of the XR display device; process,using a gesture-detection model, the gesture-based input to identify afirst gesture; receive, by the XR display device, an audio input fromthe first user, wherein the audio input comprises a first voice command;process, using a natural-language model, the audio input to identify oneor more intents or one or more slots associated with the first voicecommand; determine whether the identified first gesture matches thefirst voice command; and execute, responsive to the identified firstgesture matching the first voice command and by the XR display device, afirst task corresponding to the first voice command based on theidentified first gesture and the identified one or more intents or oneor more slots.
 14. The media of claim 13, wherein the software isfurther operable when executed to: receive a tertiary input, wherein thetertiary input comprises one or more of a touch input, gaze input, orpose input; and determine whether the tertiary input matches the firstvoice command, wherein executing the first task is further based on thetertiary input.
 15. The media of claim 14, wherein executing the firsttask is further based on an order of receiving the gesture-based input,the audio input, and the tertiary input.
 16. The media of claim 13,wherein the software is further operable when executed to: place one ormore microphones of the XR display device into a listening moderesponsive to identifying the first gesture.
 17. A system comprising:one or more processors; and a non-transitory memory coupled to theprocessors comprising instructions executable by the processors, theprocessors operable when executing the instructions to: receive, by a XRdisplay device, a gesture-based input from a first user of the XRdisplay device; process, using a gesture-detection model, thegesture-based input to identify a first gesture; receive, by the XRdisplay device, an audio input from the first user, wherein the audioinput comprises a first voice command; process, using a natural-languagemodel, the audio input to identify one or more intents or one or moreslots associated with the first voice command; determine whether theidentified first gesture matches the first voice command; and execute,responsive to the identified first gesture matching the first voicecommand and by the XR display device, a first task corresponding to thefirst voice command based on the identified first gesture and theidentified one or more intents or one or more slots.
 18. The system ofclaim 17, wherein the processors are further operable when executing theinstructions to: receive a tertiary input, wherein the tertiary inputcomprises one or more of a touch input, gaze input, or pose input; anddetermine whether the tertiary input matches the first voice command,wherein executing the first task is further based on the tertiary input.19. The system of claim 18, wherein executing the first task is furtherbased on an order of receiving the gesture-based input, the audio input,and the tertiary input.
 20. The system of claim 17, wherein theprocessors are further operable when executing the instructions to:place one or more microphones of the XR display device into a listeningmode responsive to identifying the first gesture.